Patch-Wise Partial Face Recognition Using Convolutional Neural Network

Automatic face recognition still suffers from some problems in the real-world scenarios such as occlusion. Hence, identifying the face from its partial appearance is a challenging issue as yet. To address this, issue many methods have been proposed using traditional feature extraction techniques. In this paper, a partial face recognition problem has been tackled through utilizing patch-wise matching with Convolutional Neural Network (CNN). Firstly, a gallery images are divided into local patches, and each patch is regarded as an independent image. Then, AlexNet architecture is utilized for training image patches. The Instance-To-Class (ITC) matching technique using K-Nearest Neighbour (KNN) algorithm specifies the class of the facial test image based on patch prediction. The notable contributions of our work are two-folds: the first one is employing ITC technique for patch prediction and the last one is adopting a deep learning technique for feature extraction and handling partial occlusion problem. The achieved accuracies on two de-facto datasets show that our method outperforms several existing methods that use hand-designed feature descriptors.