Learning Deep Features for Hierarchical Classification of Mobile Phone Face Datasets in Heterogeneous Environments
Neeru Narang, Michael Patrick Martin, Dimitris Metaxas, Thirimachos Bourlai · 2017
In this paper, we propose a convolutional neural network (CNN) based, scenario-dependent and sensor (mobile device) adaptable hierarchical classification framework. Our proposed framework is designed to automatically categorize face data captured under various challenging conditions, before the FR algorithms (pre-processing, feature extraction and matching) are used. First, a unique multi-sensor database (using Samsung S4 Zoom, Nokia 1020, iPhone 5S and Samsung S5 phones) is collected containing face images indoors, outdoors, with yaw angle from -90° to +90° and at two different distances, i.e. 1 and 10 meters. To cope with pose variations, face detection and pose estimation algorithms are used for classifying the facial images into a frontal or a non-frontal class. Next, our proposed framework is used where tri-level hierarchical classification is performed as follows: Level 1, face images are classified based on phone type; Level 2, face images are further classified into indoor and outdoor images; and finally, Level 3 face images are classified into a close (1m) and a far, low quality, (10m) distance categories respectively. Experimental results show that classification accuracy is scenario dependent, reaching from 95 to more than 98% accuracy for level 2 and from 90 to more than 99% for level 3 classification. A set of experiments is performed indicating that, the usage of data grouping before the face matching is performed, resulted in a significantly improved rank-1 identification rate when compared to the original (all vs. all) biometric system.