Multiple Biometrics Use TRIZ Cases

Parallel CMOS logic under a memory array compares biometric subsets to stored templates, speeding authentication while improving reliability.

Combining image and video cues, the BiMoTranS model improves face liveness detection against Deepfake and other biometric spoofing attacks.

Computer vision and CNN models identify multiple employees and time actions at once, cutting clock-in queues and processing delays.

Image-based recognition captures multiple employees at once to automate clock-in events, cut wait times, and raise time recording throughput.

Links high-accuracy features captured in normal posture with continuous tracking features to keep person identification reliable during movement.

Fusing face and body features improves identity recognition accuracy in complex scenes with occlusion, large angles, and time-based changes.

Normal-posture features captured by environmental cameras are linked to prior biometric data to maintain accurate continuous authentication.

Intersection and XOR embeddings separate shared and distinct biometric features to detect cross-modality mismatches with lower compute and memory use.

Modality-specific matching separates mixed biometric records, repairs crosslinks, and minimizes orphaned records for identification systems.

Variable lighting and clothing can weaken single-mode biometrics; combining iris, facial, and 3D body data strengthens access-control identification.

Automated checks assess focus, pose, cropping, facial features, and soft biometrics for consistent standards-aligned records.

A face-centered hand-detection area lets one image capture both biometric features, reducing attention to multiple cameras.

Image quality weights prioritize stronger color, infrared, or depth matches, improving user account identification when offline photos vary.

AI compares biometric, device, and interaction patterns with user profiles to detect spoofing and trigger stronger verification.

This case separates biometric modalities and selectively compares records to detect and repair crosslinks without exhaustive combinations.

Automatic height-based positioning aligns facial and fingerprint capture for people standing, seated, or using wheelchairs.

An intelligent signature pen records signing and surrounding images, replacing costly on-site video equipment with remote review.

Temporal and spatial biometric consistency checks verify one-person binding before automated enrollment or authentication.

A quantum algorithmic process analyzes micropatterns of human behavior to detect system intrusions and malfunctions.

Combining iris pattern recognition with real-time eye movement analysis defeats printed image spoofing while maintaining high identification accuracy.

Smart cards generate paired codes from electronic data and biometric readings to eliminate remote server exchanges that increase fraud risk.

A biometric authentication system uses quantization indices to select volatile threshold switches for generating query vectors.

A biometric authentication system captures multiple features simultaneously to enhance accuracy.

A face recognition system filters time-varying gray-scale signals to detect biological authenticity.

A facial recognition system detects pulse and blood circulation to verify user authenticity.

Bezier approximations create unique polygons from ridge contours, enabling identification of latent prints lacking sufficient minutiae.

Concatenating biometric data with unique identifiers reduces false negatives during server-side authentication while maintaining security.

Dynamic 3D models adjust orientation and position to minimize variance with input images, resolving low matching accuracy across different angles.

Information processing apparatus acquires person region features and displays collation results on a single screen.

Multiple image sensors feed dedicated processors for parallel template extraction, reducing processing time and infrastructure needs.

Dual machine learning frameworks process sample and degraded images to optimize classification accuracy through shared parameters.

Four-factor authentication system uses facial recognition and emotion analysis to enforce security rules on intelligent bank cards.

A biometric authentication system captures contemporaneous signature images to verify user identity during electronic document signing.

A biometric authentication device employs a high-reproducibility sensor for initial verification and a lower-cost sensor for subsequent checks.

Multi-modal authentication system synchronizes facial, voice, and behavioral inputs across devices to reduce fraud in high-risk transactions.

Multi-wavelength illumination modules enhance contrast of subsurface vein patterns and surface fingerprints, eliminating physical sensor contact risks.

Converts biometric templates into searchable text strings to eliminate proprietary matcher reliance and reduce storage overhead.

Segmented database indexing with parallel cloud processing reduces search time while maintaining high matching accuracy.

Server-based biometric matching replaces complex password entry, resolving security and usability trade-offs in network access authentication.

A synthesized voice authorization device modifies user audio signals with randomized frequencies to generate unique identifiers.