NSU Research Contributions
Title : Face Recognition Time Reduction Based on Partitioned Faces without Compromising Accuracy and a Review of state-of-the-art Face Recognition Approaches
Authors : Adnan Firoze, Tonmoay Deb
Abstract : In this paper, the main objective is to make face recognition system faster by reducing recognition time without compromising accuracy for a constrained environment ie 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.
Journal : | Volume : | Year : 2018 | Issue : |
Pages : 14-21 | City : Hong Kong | Edition : | Editors : |
Publisher : ACM | ISBN : | Book : | Chapter : |
Proceeding Title : 2018 International Conference on Image and Graphics Processing (ACM) | Institution : | Issuer : | Number : |