Parallel CPU GPU Facial Recognition for Mobile Terminal Speed
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Solution Overview
Problem
Mobile terminals face security risks due to slow facial recognition speeds, which can compromise personal information and property security when unauthorized users access the device.
Innovation Solution
The method involves utilizing both the central processing unit (CPU) and graphics processing unit (GPU) to perform feature matching and liveness detection in parallel, reducing the overall execution time of facial recognition by dividing the process into feature matching and liveness detection, and further optimizing by performing depth of field and blink detection in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If facial recognition is performed using traditional sequential processing, then authentication accuracy is maintained, but recognition speed is slow
Solution Approach 1:
The patent divides facial recognition into two independent modules: feature matching (performed by CPU) and liveness detection (performed by GPU). These modules are executed in parallel rather than sequentially, allowing simultaneous processing of face identification and authenticity verification, thereby reducing total execution time while maintaining accuracy
Solution Approach 2:
The patent introduces a dual-processor architecture where CPU and GPU act as intermediaries for different processing tasks. The CPU handles feature extraction and matching while the GPU performs liveness detection through blink and depth analysis. This intermediary division allows optimal utilization of each processor's strengths and enables parallel execution
2Reliability
If comprehensive liveness detection is performed, then anti-spoofing accuracy is improved, but processing time increases
Solution Approach 1:
Liveness detection is segmented into specific sub-tasks (blink detection, depth of field detection) that are independently processed by the GPU. This segmentation allows comprehensive anti-spoofing verification without requiring sequential execution of all checks, as they can run in parallel
Solution Approach 2:
The patent replaces traditional sequential mechanical processing with GPU-accelerated parallel processing. The GPU's ability to perform multiple calculations simultaneously substitutes for sequential CPU-based liveness checks, maintaining comprehensive detection accuracy while reducing processing time
Data Source
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AI summary
Embodiments of the present disclosure relate to mobile terminal technologies, a facial recognition method and related products are provided. The method includes: acquiring a face image by a mobile terminal (100); extracting face feature information from the face image and matching the face feature information with a predetermined face feature template by a central processing unit (110) of the mobile terminal (100); performing liveness detection according to the face image by a graphics processing unit (120) of the mobile terminal (100) when the CPU (110) extracts the face feature information from the face image and matches the face feature information with the predetermined face feature template.