From Fingerprints to Heartbeats: The Shift to Non-Cooperative Biometrics

1. Introduction: The Shift to Non-Cooperative Identification

The concept of personal identity, once a philosophical abstraction anchored in the continuity of memory and consciousness, has been radically reconfigured in the twenty-first century into a tangible, harvestable commodity. For decades, the verification of identity—authentication—was a cooperative act. A subject placed a finger on an ink pad or a glass platen; a traveler paused before a camera at a border control booth; a suspect allowed a technician to swab the inside of their cheek. These acts required a moment of complicity, however coerced, between the observer and the observed.

The trajectory of biometric research and deployment throughout 2024 and the first half of 2025, however, signals a decisive and irreversible rupture with this cooperative tradition. We have entered the era of "non-cooperative" or "passive" identification. In this new paradigm, the human subject is no longer a participant in their own authentication but an object of remote analysis, often entirely unaware that their biological data—the rhythm of their walk, the geometry of their occluded face, the distinct acoustic signature of their heartbeat—is being harvested, processed, and matched against vast digital galleries.¹

This transformation is not merely an incremental improvement in sensor resolution or processing speed; it represents a fundamental architectural shift in the surveillance state, driven by the convergence of three distinct technological vectors. First, the miniaturization and ubiquity of high-frequency sensors, specifically millimeter-wave radar and laser vibrometry, have granted machines the ability to perceive physiological signals that are invisible to the naked eye and penetrative of physical barriers.³ Second, the revolution in computational architectures—moving from standard convolutional neural networks to transformer-based models and neuromorphic computing—has enabled systems to process these complex, noisy biological signals with a level of pattern recognition that mimics biological cognition.⁵ Third, the integration of generative artificial intelligence into forensic workflows has allowed investigators to reconstruct, and in some cases "hallucinate," high-fidelity data from degraded or partial evidence, challenging the very evidentiary standards of truth in the legal system.⁷

Simultaneously, the governance of these technologies has fractured along geopolitical lines, creating a global "splinternet" of biometric regulation. While the European Union has moved to strictly cordon off real-time remote biometric identification in public spaces through the full implementation of the AI Act in 2025 ⁹, the United States has seen a dramatic rescission of restrictive executive orders. The early months of 2025 in Washington were characterized by an "accelerationist" policy framework, prioritizing technological dominance and law enforcement efficacy over the precautionary principles that had begun to take root in previous years.¹⁰

This report provides an exhaustive, multi-disciplinary analysis of these advancements. It dissects the technical mechanisms of the latest biometric modalities, evaluates the empirical evidence regarding their accuracy and bias, and interrogates the profound civil liberties implications of a world where the body itself has become a password that cannot be changed, hidden, or revoked.

2. Computational Foundations: The AI Architectures of 2025

To understand the capabilities of modern biometric systems, one must first understand the shift in the underlying artificial intelligence architectures that power them. The years 2024 and 2025 saw the industry move away from the "black box" reliance on simple Convolutional Neural Networks (CNNs) toward more dynamic, generative, and biologically inspired computing models.

2.1 Contrastive Learning and Vector Embeddings

At the heart of modern recognition systems—whether for faces, gait, or voices—lies the concept of "representation learning." The goal is not just to classify an image (e.g., "this is a face"), but to map that image into a high-dimensional mathematical space where similar things are close together and dissimilar things are far apart. This is achieved through Contrastive Learning.

In 2024-2025, frameworks like SimCLR (Simple Contrastive Learning of Representations) became the standard for training robust biometric models. The mechanism is conceptually simple but computationally intensive. The system takes an image (an "anchor") and creates a modified version of it (a "positive") by applying distortions like cropping, blurring, or color changing. It also takes images of different people ("negatives"). The neural network is then trained to pull the vector representations of the anchor and the positive closer together while pushing the negatives away.¹²

This process creates a "latent space" where the identity of a person is preserved regardless of lighting, pose, or partial occlusion. For law enforcement, this means a probe image from a grainy night-vision camera can successfully match a high-quality booking photo because the algorithm has learned to ignore the "style" of the image (lighting, noise) and focus on the "content" (identity).¹⁴ The mathematical objective, often a variant of "triplet loss" or "contrastive loss," forces the model to learn a manifold where the distance between points corresponds to the semantic similarity of the identities.¹⁵

2.2 The Rise of Vision Transformers (ViT)

For over a decade, CNNs were the gold standard for image analysis. However, CNNs have a "local" bias—they process an image by looking at small patches of pixels and slowly building up to a whole. This makes them less effective at understanding long-range relationships, such as how the swing of a left arm relates to the planting of a right foot in a gait cycle.

In 2025, Vision Transformers (ViTs) largely supplanted CNNs in complex biometric tasks. Originally designed for language processing (like the models powering ChatGPT), transformers use a mechanism called Self-Attention. When processing a video of a person walking, a transformer allows the model to look at every frame and every body part simultaneously, assigning "attention weights" to the most relevant features.⁵

This global receptive field allows the system to understand the context of movement. For example, if a subject is carrying a heavy bag, their gait changes. A CNN might misidentify this as a different person. A transformer, however, can "attend" to the bag and the compensatory muscle movements, understanding that the core identity signature remains the same despite the altered gait. This architectural shift has been crucial in making behavioral biometrics viable for real-world surveillance.¹⁶

2.3 Generative Diffusion Models in Forensics

Perhaps the most controversial technical leap is the application of Diffusion Probabilistic Models to forensic image restoration. Unlike previous enhancement techniques that simply sharpened edges, diffusion models work by learning the statistical distribution of "perfect" face images.

The training process involves taking high-quality images and progressively destroying them by adding Gaussian noise until they are just random static. The model then learns to reverse this process—to take static and sequentially remove the noise to reveal a coherent image. In 2025, models like SDFace (based on Stable Diffusion XL) were adapted for "blind face restoration." When presented with a blurry, pixelated surveillance frame, the model uses the degraded image as a guide to "denoise" a latent representation, effectively hallucinating high-frequency details—pores, eyelashes, iris texture—that were never present in the original file.⁷

The result is an image that is perceptually indistinguishable from a high-definition photograph. However, the forensic validity of this "hallucinated" detail is fiercely debated. If the model fills in a gap in the eyebrow because statistically, most people have continuous eyebrows, but the suspect actually has a scar there, the restoration has created false evidence.¹⁰

2.4 Neuromorphic Computing: The Edge of the Network

Finally, the push for "always-on" passive surveillance has driven the adoption of neuromorphic computing. These chips are designed to mimic the biological structure of the human brain, using "spiking neural networks" (SNNs). In a standard computer (von Neumann architecture), data moves back and forth between memory and the processor, consuming energy and creating latency. In a neuromorphic chip, memory and processing are collocated in artificial "neurons" and "synapses".⁶

Crucially, SNNs are "event-driven." They do not process every pixel of every frame of a video. They only "fire" when a change is detected (e.g., a movement, a heartbeat vibration). This massive efficiency allows sophisticated biometric processing to happen on "edge" devices—smart cameras, wearables, or handheld police scanners—without needing to transmit data to the cloud. This reduces bandwidth costs and, paradoxically, offers a form of privacy protection (data stays on the device) while simultaneously enabling ubiquitous sensing.¹⁸

3. Advanced Facial Recognition: Restoration, Occlusion, and Bias

Facial Recognition Technology (FRT) remains the primary biometric modality for law enforcement, but the nature of the "face" being analyzed has changed. The days of requiring a perfectly lit, frontal mugshot are fading. The frontier of 2025 is the recognition of the occluded, the degraded, and the distant.

3.1 The "Hallucination" of Evidence: Generative Face Restoration

The NTIRE 2025 (New Trends in Image Restoration and Enhancement) Challenge on Real-World Face Restoration highlighted the industry's pivot toward generative AI. The challenge tasked participants with recovering high-quality face images from "real-world" degraded inputs—images affected by low light, compression artifacts, and motion blur.⁸

The winning solutions utilized large-scale pre-trained diffusion models. These models, such as the SDFace framework, integrate a "prior"—a mathematical understanding of what a human face should look like. When restoring an image, the model balances two competing objectives:

1. Fidelity: Staying true to the low-resolution input.

2. Realism: Ensuring the output looks like a high-quality photograph.

To prevent the model from generating a generic "average" face, researchers incorporated identity-preserving modules like AdaFace. This component extracts a vector embedding from the low-quality input and forces the generative model to produce an output that, when analyzed, yields a similar vector.²⁰

Table 1: Evolution of Forensic Image Enhancement

Era	Technique	Mechanism	Forensic Risk
2010s	Bicubic Interpolation	Mathematical averaging of pixels.	Blurry results; low detail.
2018-2022	GANs (Generative Adversarial Networks)	Generator vs. Discriminator game.	Mode collapse; creating artifacts (e.g., extra teeth).
2024-2025	Diffusion Models (e.g., SDFace)	Iterative denoising from latent noise.	"Hallucination": Inventing realistic but non-existent features (scars, moles).

The legal implications are profound. If a generative model restores a blurry license plate or a suspect's face, defense attorneys are increasingly challenging the admissibility of this evidence, arguing it is "AI-generated art" rather than a forensic enhancement. In response, best practices emerging in 2025 suggest that while these tools can be used for investigative leads (generating a poster to get tips), they should never be used for positive identification in court without corroborating biometric matches.¹⁰

3.2 Masked Face Recognition (MFR): The Pandemic Legacy

The COVID-19 pandemic effectively broke traditional facial recognition systems, which relied heavily on the geometry of the nose, mouth, and chin. In response, the biometric community rapidly developed Masked Face Recognition (MFR). By 2025, these systems had matured from experimental patches to robust commercial products.²²

The dominant technical approach utilizes Siamese Networks focused on the periocular region (the eyes, eyebrows, and forehead). Rather than treating a mask as an "error" or occlusion to be ignored, modern algorithms use "attention mechanisms" to dynamically down-weight the masked pixels and up-weight the unoccluded regions. Some systems even employ a "teacher-student" training method, where a "teacher" network sees the full face and guides a "student" network that sees only the masked face, forcing the student to learn to identify the person solely from the eyes.²³

However, this reliance on the periocular region has exacerbated demographic biases. The U.S. Commission on Civil Rights (USCCR) reported in September 2024 that while top-tier algorithms have error rates below 1% for optimal images, MFR systems still struggle with specific populations. In particular, false negative rates (failing to identify a match) remained higher for Asian and American Indian individuals in domestic arrest databases when masks were involved.²⁵ This is likely due to the reduced feature set available in the periocular region, where distinct morphological variations between ethnic groups may be less pronounced or less well-represented in training datasets.

3.3 Accuracy and Demographic Disparities

The discourse around FRT accuracy has bifurcated. On one hand, the National Institute of Standards and Technology (NIST) evaluations show that the best algorithms are astoundingly accurate, capable of distinguishing identical twins in some cases. On the other hand, real-world performance—especially in the "wild" of surveillance video—lags significantly.

The USCCR's 2024 investigation found that "false positive" rates (incorrectly matching a person to a suspect) were significantly higher for West and East African and East Asian individuals compared to Eastern Europeans. In some cases, the error rate differential was a factor of 100.²⁵ This statistical disparity translates into real-world harm: the wrongful arrest of innocent individuals who happen to share a similar facial structure with a suspect. The Commission noted that these errors are not merely technical glitches but civil rights violations, particularly when used by agencies like the Department of Housing and Urban Development (HUD) to police public housing access without rigorous oversight.²⁵

4. The Body in Motion: Behavioral Biometrics and Gait Analysis

As facial recognition faces scrutiny and countermeasures, law enforcement has turned to the body itself. Gait analysis—identifying a person by the unique biomechanics of their walk—has emerged as a leading "standoff" biometric, capable of identifying subjects from hundreds of meters away, even from behind or in low light.²⁶

4.1 From Silhouettes to Skeletal Pose Estimation

Early gait analysis relied on "binary silhouettes"—essentially looking at the black-and-white shadow of a walker. This was easily confused by baggy clothing or carrying objects. The breakthrough in 2024-2025 was the integration of Model-Based Gait Recognition using 3D skeletal pose estimation.

Modern systems use cameras to map the specific coordinates of joints (shoulders, elbows, hips, knees, ankles) in three-dimensional space. This creates a "skeleton" that is independent of clothing texture or color. Algorithms then analyze the dynamic relationship between these joints over time. For example, they measure the angle of the knee at the point of heel strike, or the specific rotational velocity of the hip swing.²⁷

4.2 The Transformer Revolution in Gait

The shift to Vision Transformers (ViT) has been pivotal here. A gait cycle is a temporal sequence. A transformer-based model, like GaitTriViT, processes this sequence non-linearly. It can "attend" to the specific way a subject's foot pronates (rolls inward) and link that to the sway of their opposite shoulder, creating a holistic "body print".⁵

To handle the problem of "hard negatives" (people who walk similarly), researchers introduced Cross-Attention Re-ranking. In systems like CarGait, once a list of potential suspects is retrieved, the system performs a second, deeper pass. It uses cross-attention layers to compare the fine-grained motion patterns of the suspect against the database candidates, effectively asking, "Do these two skeletons move with the same underlying physics?" This method has significantly improved Rank-1 accuracy (the ability to get the right match on the first try).²⁷

4.3 Market Growth and Police Adoption

The market for gait biometrics is expanding rapidly, projected to grow from $109 million in 2024 to over $300 million by 2034.²⁶ This growth is not just theoretical. In 2025, reports surfaced of US police departments, including those in Seattle and Berkeley, engaging with vendors for "surveillance technology" that explicitly lists gait analysis capabilities.²⁹

In the UK, the "Police Emerging Science and Technology Trends" report outlined a future where gait analysis is used for "mass scanning" of crowds to identify troublemakers or known offenders who might be masking their faces.³¹ This capability fundamentally alters the nature of public anonymity. While one can wear a mask or sunglasses to hide a face, changing one's gait requires a conscious, sustained physical effort that is difficult to maintain over time, especially when unaware of being watched.²

5. The Invisible Pulse: Cardiac and Through-Wall Biometrics

If gait analysis strips away the anonymity of movement, cardiac biometrics strips away the privacy of the internal body. The ability to identify a person by their heartbeat—remotely and without contact—represents the cutting edge of physiological surveillance.

5.1 The Unique Cardiac Signature

The human heart is not a simple metronome. It is a complex mechanical pump. The specific size, shape, and elasticity of an individual's heart valves and chambers create a unique acoustic and vibrational signature. This signature includes the "lub-dub" sounds (phonocardiogram) and the electrical depolarization patterns (ECG). Research has shown that these patterns are as unique as a fingerprint and significantly harder to forge.³

5.2 Contactless Sensing: Radar and Lasers

Two primary technologies have emerged to capture this signature remotely:

1. Millimeter-Wave (mmWave) Radar: Operating at frequencies like 60 GHz, these sensors emit radio waves that reflect off the human body. The chest wall moves only a fraction of a millimeter due to the heartbeat, but the Doppler shift in the reflected wave can capture this motion. In 2025, systems using Conditional Variational Autoencoders (CVAEs) achieved authentication accuracy of over 97% in testing. The AI helps separate the tiny heartbeat signal from the larger motions of breathing or body shifting.³

2. Laser Vibrometry: Researchers at the University of Glasgow developed a system using a laser to measure the nanoscopic vibrations of the skin on the throat. This system, powered by a neuromorphic chip, processes the "heart sounds" directly on the device. Because it uses event-driven spiking neural networks, it is incredibly energy-efficient and fast.³³

5.3 Seeing Through Walls: The DePLife Program

The Department of Homeland Security (DHS) has aggressively pursued the militarization of this technology. The Detection of Presence of Life (DePLife) program has commercialized radar units that can detect a heartbeat through solid barriers like concrete and brick.

Originally designed for search and rescue (finding victims in rubble), the technology has been adapted for tactical law enforcement. In mid-2025, the DHS Science and Technology Directorate and MIT Lincoln Laboratory completed upgrades to these units, incorporating motion compensation algorithms. This allows a SWAT team member to hold the device (even if their hands are shaking) and scan a room through a wall to determine if a suspect is inside and exactly where they are standing based on their heart rate.⁴

Table 2: Comparison of Remote Cardiac Technologies

Feature	mmWave Radar (60 GHz)	Laser Vibrometry
Principle	Radio wave Doppler shift of chest wall.	Optical measurement of skin vibration.
Range	Short to Medium (Through clothing/walls).	Long range (Line of sight required).
Primary Use	Through-wall detection; ID verification.	Long-distance ID; health monitoring.
AI Architecture	CVAE / Deep Learning.	Neuromorphic / Spiking Neural Networks.
Privacy Risk	Can scan without line of sight (invisible).	Requires line of sight; high precision.

The civil liberties implications are stark. The US Supreme Court (e.g., Kyllo v. United States) has historically held that using thermal imaging to see inside a home constitutes a "search" requiring a warrant. However, the transient and exigent nature of tactical situations often bypasses warrant requirements. The ability to "frisk" a building for human presence remotely effectively renders physical privacy obsolete in the face of police power.

6. Genomic Surveillance: The Speed of Identity

While AI and sensors track the living, genetic technologies are revolutionizing the identification of the biological self. The year 2025 marked the full integration of "Rapid DNA" into the US justice system and the controversial expansion of DNA phenotyping.

6.1 The "Genetic Stop-and-Frisk": Rapid DNA in CODIS

On July 1, 2025, the FBI formally allowed the integration of Rapid DNA profiles into the National DNA Index System (NDIS) via CODIS. Rapid DNA instruments are automated, "swab-in, profile-out" machines. A police officer places a cheek swab into a cartridge, and within 90 minutes, the machine performs the extraction, amplification, and analysis that used to take a lab weeks.³⁶

This policy change allows booking stations to enroll arrestees and check them against the database of unsolved crimes during the booking process. A suspect arrested for a minor offense, like trespassing or shoplifting, can be immediately linked to a cold case homicide or sexual assault before they are released on bail.³⁸

While proponents argue this prevents dangerous criminals from slipping through the cracks, civil rights advocates fear a "net-widening" effect. If DNA collection becomes as fast and routine as fingerprinting, police may be incentivized to make pretextual arrests for minor infractions solely to harvest DNA from populations of interest. This "genetic stop-and-frisk" raises significant Fourth Amendment concerns regarding the seizure of biological data without a warrant for the specific purpose of a general search.³⁹

6.2 Constructing the Suspect: Forensic DNA Phenotyping (FDP)

When a DNA sample does not match anyone in the database, investigators are turning to Forensic DNA Phenotyping (FDP). This technology attempts to predict a suspect's physical appearance from their genetic code.

Companies like Parabon NanoLabs offer services that predict eye color, hair color, skin complexion, and—most controversially—facial morphology (face shape). They produce "Snapshot" reports: composite sketches of what the suspect might look like.⁴⁰

The Scientific Controversy: The validity of predicting pigmentation (eyes, hair, skin) is relatively well-supported by science, with area-under-the-curve (AUC) accuracy values often exceeding 0.90.⁴¹ However, predicting face shape is a different matter. Facial morphology is "polygenic," influenced by hundreds or thousands of genes, as well as environmental factors (nutrition, aging, weight).

Critics, including geneticist Dr. Susan Walsh, argue that the science for accurate face shape prediction "isn't there yet".⁴² They point out that Parabon's methodology is proprietary and has not been subjected to rigorous, independent peer review. The company claims "accuracy" based on internal validation, but the lack of transparency prevents the scientific community from verifying these claims.⁴²

Operational Risks: Despite the scientific skepticism, these "genetic sketches" are used to generate leads. The danger is confirmation bias. If a Snapshot report suggests a suspect is a "fair-skinned male with a narrow jaw," police may focus exclusively on individuals matching that description, potentially ignoring the actual perpetrator if the prediction is flawed. Furthermore, there have been instances of police taking these genetic approximations and running them through facial recognition software—a practice described by experts as "turbo-charging bad ideas" by combining a probabilistic guess (phenotyping) with a probabilistic search (FRT).⁴⁴

7. The Geopolitics of Biometric Governance: A Global Splinternet

The year 2025 has revealed a stark divergence in how the world's major powers govern these powerful technologies. We are witnessing the formation of a regulatory "splinternet," where the legality of your biometric self depends entirely on where you stand.

7.1 The United States: Deregulation and "Dominance"

In the United States, the regulatory pendulum swung violently in early 2025. On January 20, the incoming administration issued Executive Order 14148, explicitly revoking the previous administration's "Safe, Secure, and Trustworthy AI" order (EO 14110). This was followed by Executive Order 14179, titled "Removing Barriers to American Leadership in Artificial Intelligence".¹⁰

The new US policy framework is characterized by:

• Deregulation: The removal of "cumbersome" safety testing and equity assessments that were viewed as stifling innovation.¹¹

• National Security Framing: Biometrics and AI are positioned not as consumer products requiring safety labels, but as strategic assets in a great power competition. The rhetoric emphasizes "dominance" and "winning the race".⁴⁷

• Law Enforcement Empowerment: The Department of Justice and DHS are encouraged to leverage these tools to enhance public safety. While the December 2024 DHS Biometric Technology Report laid out "best practices" (such as human adjudication of matches), the rescission of the executive orders underpinning them leaves their enforceability in question.⁴⁹

7.2 The European Union: The Rights-Based Firewall

Across the Atlantic, the European Union has taken the opposite path. The EU AI Act became fully applicable for prohibited practices in February 2025. Article 5 of the Act creates a "firewall" around public spaces.⁹

Prohibited Practices in the EU:

• Real-time Remote Biometric Identification (RBI) in publicly accessible spaces by law enforcement is banned, with very narrow exceptions (e.g., stopping an imminent terror attack or finding a missing child) that require judicial authorization.⁵⁰

• Biometric Categorization: Systems that infer sensitive data (race, political opinion, sexual orientation) from biometrics are prohibited.

• Emotion Recognition: The use of AI to detect emotions in law enforcement or border management contexts is banned.⁹

This divergence creates a complex landscape for global technology vendors. A company developing a gait analysis system in Seattle can sell it to the NYPD for routine street surveillance but cannot sell the same configuration to the police in Berlin or Paris. This may lead to a bifurcation in the technology itself, with "Western" democratic products splitting into "US-compliant" (high surveillance capability) and "EU-compliant" (privacy-preserving) variants.

7.3 Civil Rights and the "Wild West" of Deployment

In the US, the lack of comprehensive federal legislation has left a vacuum. The US Commission on Civil Rights warned in its September 2024 report that this vacuum is being filled by unchecked agency discretion. The report highlighted that federal agencies like HUD were funding the installation of facial recognition in public housing without any standardized policies, potentially subjecting low-income residents to a surveillance regime that wealthier citizens do not face.²⁵

The Commission's findings on accuracy were damning: despite technical improvements, FRT systems still exhibit significant demographic disparities. The "false positive" rate for African American and Asian faces remains higher than for White faces, meaning these groups are more likely to be misidentified as criminals.²⁵ In a deregulated environment, the burden of challenging these errors falls on the individual citizen, often after the harm of a wrongful arrest has already occurred.

8. Conclusion: The Era of Probabilistic Justice

The advancements of 2024 and 2025 represent a fundamental transformation in the nature of evidence and identity. We are moving from a world of deterministic biometrics (a fingerprint match is a binary fact) to a world of probabilistic biometrics.

When a diffusion model restores a face, it is making a statistical guess based on millions of other faces. When a transformer analyzes a gait, it is calculating a probability of similarity. When a DNA phenotype report draws a face, it is offering a genetic likelihood, not a photograph.

The danger, as these technologies flood into the legal system, is that the "CSI Effect" will blind juries and investigators to this distinction. A computer-generated image looks authoritative; a percentage score looks like math. But if that math is built on "hallucinated" details or proprietary, unvetted algorithms, the justice system risks becoming a machine for confirming its own biases.

As we look toward the latter half of the decade, the "Panopticon" is no longer a metaphor for a guard tower in the center of a prison. It is the sensor in the wall that hears your heart; the camera on the corner that knows your walk; and the algorithm in the cloud that can reconstruct your face from a blur. The technology has arrived. The question that remains is whether our laws and our concept of liberty can survive it.

Works cited

1. Biometric Surveillance, accessed January 21, 2026, https://sls.eff.org/technologies/biometric-surveillance

2. Behavioral biometrics: A promising tool for enhancing public safety - Help Net Security, accessed January 21, 2026, https://www.helpnetsecurity.com/2021/11/29/behavioral-biometrics/

3. Heartbeat detection and personal authentication using a 60 GHz Doppler sensor - Frontiers, accessed January 21, 2026, https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1570144/full

4. Feature Article: Testing New Technologies that Help Law Enforcement See Through Walls, accessed January 21, 2026, https://www.dhs.gov/science-and-technology/news/2025/06/10/feature-article-testing-new-technologies-help-law-enforcement-see-through-walls

5. GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition - NIH, accessed January 21, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC12453820/

6. What Is Neuromorphic Computing? | Built In, accessed January 21, 2026, https://builtin.com/artificial-intelligence/neuromorphic-computing

7. NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results - arXiv, accessed January 21, 2026, https://arxiv.org/html/2504.14600v1

8. zhengchen1999/NTIRE2025_RealWorld_Face_Restoration - GitHub, accessed January 21, 2026, https://github.com/zhengchen1999/ntire2025_realworld_face_restoration

9. EU AI Act - Updates, Compliance, Training, accessed January 21, 2026, https://www.artificial-intelligence-act.com/

10. DOJ Report on AI in Criminal Justice: Key Takeaways, accessed January 21, 2026, https://counciloncj.org/doj-report-on-ai-in-criminal-justice-key-takeaways/

11. Ensuring a National Policy Framework for Artificial Intelligence - The White House, accessed January 21, 2026, https://www.whitehouse.gov/presidential-actions/2025/12/eliminating-state-law-obstruction-of-national-artificial-intelligence-policy/

12. Full Guide to Contrastive Learning | Encord, accessed January 21, 2026, https://encord.com/blog/guide-to-contrastive-learning/

13. Contrastive Learning - SimCLR and BYOL (With Code Example), accessed January 21, 2026, https://learnopencv.com/contrastive-learning-simclr-and-byol-with-code-example/

14. Contrastive Learning for Image Classification | by Amit Yadav | Biased-Algorithms | Medium, accessed January 21, 2026, https://medium.com/biased-algorithms/contrastive-learning-for-image-classification-82d3aebfdd55

15. Contrastive Learning and Facial Recognition, accessed January 21, 2026, https://www.usna.edu/Users/cs/SD312/notes/19Contrastive

16. (PDF) Gait-ViT: Gait Recognition with Vision Transformer - ResearchGate, accessed January 21, 2026, https://www.researchgate.net/publication/364366527_Gait-ViT_Gait_Recognition_with_Vision_Transformer

17. Neuromorphic computing - Wikipedia, accessed January 21, 2026, https://en.wikipedia.org/wiki/Neuromorphic_computing

18. A neuromorphic multi-scale approach for real-time heart rate and state detection - NIH, accessed January 21, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC11964916/

19. What Is Neuromorphic Computing? - IBM, accessed January 21, 2026, https://www.ibm.com/think/topics/neuromorphic-computing

20. NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results - arXiv, accessed January 21, 2026, https://arxiv.org/abs/2504.14600

21. Deepfakes on trial: How judges are navigating AI evidence authentication, accessed January 21, 2026, https://www.thomsonreuters.com/en-us/posts/ai-in-courts/deepfakes-evidence-authentication/

22. Inclusive Review on Advances in Masked Human Face Recognition Technologies - arXiv, accessed January 21, 2026, https://arxiv.org/abs/2508.00841

23. [PDF] Masked Face Recognition Using Deep Learning: A Review - Semantic Scholar, accessed January 21, 2026, https://www.semanticscholar.org/paper/Masked-Face-Recognition-Using-Deep-Learning%3A-A-Alzu%E2%80%99bi-Albalas/b9f971e9003450f81bf9f61dee1f2198bc7d7ad7

24. (PDF) Deep Learning-Based Siamese Neural Network for Masked Face Recognition, accessed January 21, 2026, https://www.researchgate.net/publication/392445645_Deep_Learning-Based_Siamese_Neural_Network_for_Masked_Face_Recognition

25. The Civil Rights Implications of the Federal Use of Facial ..., accessed January 21, 2026, https://www.usccr.gov/files/2024-09/civil-rights-implications-of-frt_0.pdf

26. Gait Biometrics Market Exhibits Huge Growth at 10.9%, accessed January 21, 2026, https://scoop.market.us/gait-biometrics-market-news/

27. arXiv:2503.03501v1 [cs.CV] 5 Mar 2025, accessed January 21, 2026, https://arxiv.org/pdf/2503.03501

28. Next-generation fall detection: harnessing human pose estimation and transformer technology - Taylor & Francis, accessed January 21, 2026, https://www.tandfonline.com/doi/abs/10.1080/20476965.2024.2395574

29. 2024 Privacy Impact Assessment - BLTN - Seattle.gov, accessed January 21, 2026, https://www.seattle.gov/documents/departments/tech/privacy/pia%202025/bltn_pia_2025.pdf

30. Page numbers for this agenda packet are marked in blue to distinguish them from the numbering within individual documents. - The City of Berkeley, accessed January 21, 2026, https://berkeleyca.gov/sites/default/files/legislative-body-meeting-attachments/2025-11-05%20PAB%20Regular%20Meeting%20Agenda%20Packet_final%20%281%29.pdf

31. UK police look at future tech, including biometrics like brainwaves, accessed January 21, 2026, https://www.biometricupdate.com/202506/uk-police-look-at-future-tech-including-biometrics-like-brainwaves

32. Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection - MDPI, accessed January 21, 2026, https://www.mdpi.com/1424-8220/22/19/7560

33. Your Heartbeat Could Be Your Password — And a Laser Can Read It Through Your Skin | by Bradley Susser | Medium, accessed January 21, 2026, https://medium.com/@bradleysusser/your-heartbeat-could-be-your-password-and-a-laser-can-read-it-through-your-skin-3e8d7ff1a208

34. A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection - PubMed, accessed January 21, 2026, https://pubmed.ncbi.nlm.nih.gov/34797760/

35. Testing new technologies that help law enforcement 'see' through walls | MIT Lincoln Laboratory, accessed January 21, 2026, https://www.ll.mit.edu/news/testing-new-technologies-help-law-enforcement-see-through-walls

36. Rapid DNA Evidence to Be Integrated into CODIS by 2025 - RollsTechGlobal, accessed January 21, 2026, https://rollstechglobal.com/rapid-dna-evidence-to-be-integrated-into-codis-by-2025/

37. Guide to All Things Rapid DNA - FBI.gov, accessed January 21, 2026, https://le.fbi.gov/file-repository/rapid-dna-guide-january-2022.pdf

38. Cracking cold cases in minutes: How rapid DNA technology transforms investigations, accessed January 21, 2026, https://www.police1.com/police-products/investigation/dna-forensics/cracking-cold-cases-in-minutes-how-rapid-dna-technology-transforms-investigations

39. Texas DPS Rapid DNA Analysis | Department of Public Safety, accessed January 21, 2026, https://www.dps.texas.gov/section/crime-laboratory/faq/texas-dps-rapid-dna-analysis

40. DNA Phenotyping - Parabon® Snapshot® DNA Analysis Service, accessed January 21, 2026, https://snapshot.parabon-nanolabs.com/phenotyping

41. The Use of Forensic DNA Phenotyping in Predicting Appearance and Biogeographic Ancestry - PMC - NIH, accessed January 21, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC6976916/

42. How to Build a Human; A Forensics Company Tells Cops It Can Use DNA to Predict a Suspect's Face. Scientists Worry the Tool Will Deepen Racial Bias. - Prison Legal News, accessed January 21, 2026, https://www.criminallegalnews.org/news/2025/sep/15/how-build-human-forensics-company-tells-cops-it-can-use-dna-predict-suspects-face-scientists-worry-tool-will-deepen-racial-bias/

43. Use of DNA phenotyping to solve crimes fraught with problems, critics say, accessed January 21, 2026, https://www.biometricupdate.com/202502/use-of-dna-phenotyping-to-solve-crimes-fraught-with-problems-critics-say

44. Cops Used DNA to Predict a Suspect's Face—and Tried to Run Facial Recognition on It : r/Futurology - Reddit, accessed January 21, 2026, https://www.reddit.com/r/Futurology/comments/19cvgpo/cops_used_dna_to_predict_a_suspects_faceand_tried/

45. Cops Running DNA-Manufactured Faces Through Face Recognition Is a Tornado of Bad Ideas | Electronic Frontier Foundation, accessed January 21, 2026, https://www.eff.org/deeplinks/2024/03/cops-running-dna-manufactured-faces-through-face-recognition-tornado-bad-ideas

46. AI Under the Spotlight: Key Insights Ahead of the White House Action Plan, accessed January 21, 2026, https://www.workforcebulletin.com/ai-under-the-spotlight-key-insights-ahead-of-the-white-house-action-plan

47. Key Insights on President Trump's New AI Executive Order and Policy & Regulatory Implications | Privacy World, accessed January 21, 2026, https://www.privacyworld.blog/2025/02/key-insights-on-president-trumps-new-ai-executive-order-and-policy-regulatory-implications/

48. OMB Issues First Trump 2.0-Era Requirements for AI Use and Procurement by Federal Agencies | Inside Government Contracts, accessed January 21, 2026, https://www.insidegovernmentcontracts.com/2025/04/omb-issues-first-trump-2-0-era-requirements-for-ai-use-and-procurement-by-federal-agencies/

49. BIOMETRIC TECHNOLOGY REPORT - Homeland Security, accessed January 21, 2026, https://www.dhs.gov/sites/default/files/2024-12/24_1230_st_13e-Final-Report-2024-12-26.pdf

50. Article 5: Prohibited AI Practices | EU Artificial Intelligence Act, accessed January 21, 2026, https://artificialintelligenceact.eu/article/5/

51. Review of Masked Face Recognition Based on Deep Learning - MDPI, accessed January 21, 2026, https://www.mdpi.com/2227-7080/13/7/310

52. Global Gait Biometrics Market Segmentation Analysis and Forecast to 2034 - By Component (Hardware, Software, Services), By Application, (Security & Surveillance, Healthcare, Sports & Fitness, Automotive, Others) – Leading Region (North America: 35.7% Share) and Companies, accessed January 21, 2026, https://market.us/report/global-gait-biometrics-market/

53. Ding, Yuqi (2025) Neuromorphic signal processing for wearable devices. PhD thesis. https://theses.gla.ac.uk/85340/ Copyright and, accessed January 21, 2026, https://theses.gla.ac.uk/85340/1/2025DingPhD.pdf

54. Rapid DNA: A Game-Changer for Law Enforcement, accessed January 21, 2026, https://golawenforcement.com/articles/rapid-dna/

55. Forensic DNA phenotyping: a review on SNP panels, genotyping techniques, and prediction models - PMC - PubMed Central, accessed January 21, 2026, https://pmc.ncbi.nlm.nih.gov/articles/PMC11843099/

56. DNA phenotyping: current application in forensic science - ResearchGate, accessed January 21, 2026, https://www.researchgate.net/publication/330945592_DNA_phenotyping_current_application_in_forensic_science

57. European Commission publishes guidelines on Prohibited AI Practices - DLA Piper, accessed January 21, 2026, https://www.dlapiper.com/en/insights/publications/ai-outlook/2025/european-commission-publishes-guidelines-on-prohibited-ai-practices

58. U.S. Commission on Civil Rights Releases Report: The Civil Rights Implications of the Federal Use of Facial Recognition Technology, accessed January 21, 2026, https://www.usccr.gov/news/2024/us-commission-civil-rights-releases-report-civil-rights-implications-federal-use-facial