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A Dynamic Unconstrained Feature Matching Algorithm for Face Recognition

Since the last three decades, face detection and recognition have become very active and a huge part of image processing research. Front view/direction face recognition has proved promising results with many constraints. In real-world applications like video surveillance, missing investigation, front views cannot be guaranteed as input. Hence the failure rates can degrade the performance of the face recognition system. In this paper, a new partial face recognition system proposed. It is used to overcome the drawbacks of the face recognition system, which based on the front view detection and recognition system. This partial face recognition can also be termed as Unconstrained Dynamic Feature Matching (U-DFM); it does not require prior knowledge of angle, direction, and view. The U-DFM method combines Fully Convolutional Networks (FCNs) and Ambiguity Sensitive Matching Classifier (AMC). The U-DFM addresses various face sizes problem of partial face recognition. The algorithm will be testing with CASIA-NIR-Mobile, LFW, and CAISA-NIRDistance databases and will prove better results than traditional algorithms.