Abstract
This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30 degrees on either side from the front face showed an average face recognition precision of 72.25% - 81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.
Highlights
The process of detecting and locating faces from a single or series of images and identifying the face(s) is known as face recognition
Local Binary Pattern Histogram (LBPH) architecture of face recognition is a powerful algorithm to recognize the face under varying illumination conditions and at low resolution
Our experiment results in a novel face recognition accuracy of 78.40% at the low resolution of 15 px and 98.05% at 45 px with LRD200 database
Summary
The process of detecting and locating faces from a single or series of images and identifying the face(s) is known as face recognition. Endluri et al [11] reported a real-time embedded face recognition system based on the PCA method, which supports resolution 320 × 340 pixels’ images. Schaffer et al presented [12] a face detection system that utilized a software-based (MATLAB) Viola-Jones face detection algorithm and FPGA based PCA algorithm for the face recognition part They have reported real-time face recognition at an accuracy of about 95% and can process 13,026 faces per second. Our proposed method employs the LBPH algorithm along with face alignment and CLAHE for real-time face recognition at low resolution with higher recognition accuracy. It has an improved face recognition performance with a reduced number of images person in the database. Our experimental results indicate that face recognition can achieve an improved result at only 15 px of the input image even with various attitudes and facial deflection
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