DYNAMIC FEATURE MATCHING FOR PARTIAL FACE RECOGNITION

Abstract

Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes. DFM does not require prior position information of partial faces against a holistic face. By sharing computation, the feature maps are calculated from the entire input image once, which yields a significant speedup. Experimental results demonstrate the effectiveness and advantages of DFM in comparison with state-of-the-art PFR methods on several partial face databases, including CAISA-NIR-Distance, CASIA-NIR-Mobile, and LFW databases. The performance of DFM is also impressive in partial person re-identification on Partial RE-ID and iLIDS databases.

Existing System

Fully Convolutional Network, Sparse Representation Classification

Proposed System

The proposed partial face recognition approach, Dynamic Feature Matching (DFM), combines FCN with SRC, achieving state-of-the-art performance in computational efficiency and recognition accuracy. DFM can not only work for holistic face images but also can deal with partial faces of arbitrary-size without requiring face alignment. The proposed method outperforms existing partial face recognition algorithms on several face databases. Besides, DFM achieves competitive performance on partial person re-identification and it can be extended to other computer vision problems.

Architecture

BLOCK DIAGRAM