Face Recognition Time Reduction Based on Partitioned Faces without Compromising Accuracy and a Review of state-of-the-art Face Recognition Approaches
Adnan Firoze, Tonmoay Deb · 2018
In this paper, the main objective is to make face recognition system faster by reducing recognition time without compromising accuracy for a constrained environment i.e. classroom, and provide a comparative review of state-of-the-art and classical approaches considering multiple faces that are at variable distance from the camera in the same image. This makes it a more challenging problem. Several models have been developed to partition the faces from a test image into three different levels. We have developed model hybridization by applying some classical but faster face recognition models namely Eigenfaces, Fisherfaces, Local Binary Patterns (LBP), and state-of-the-art yet relatively slower Convolutional Neural Network Model (CNN). Our proposed model hybridization technique based on different levels done by face partitioning has achieved approximately 33.43% faster performance than CNN while maintaining accuracy same as of CNN of our own dataset of faces of a classroom of 15 students while a class was going. The faces were of different students in different places, positions, poses and lighting. The objective of our research is not to enumerate and show how large a dataset we can identify by face, rather it is more interesting. We are interested in recognizing multiple faces at different distances from camera (hence, varying size, posture etc.) which calls for a unique approach as opposed to large dataset headshots from different angles of single people. We used different classification models for different levels of distance from the camera to achieve this faster response, making it a novel hybrid model.