An Improved Histogram-Based Features in Low-Frequency DCT Domain for Face Recognition

Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi
2015 International Journal of Machine Learning and Computing  
Previously, we proposed an efficient algorithm for facial image recognition combined with vector quantization (VQ) histogram and energy histogram in low-frequency DCT domains. The former algorithm is essential for utilizing the phase information of DCT coefficients by applying binary vector quantization (BVQ) on DCT coefficient blocks. The latter algorithm, energy histogram can be considered to add magnitude information of DCT coefficients. These two histograms, which contain both phase and
more » ... itude information of a DCT transformed facial image, are utilized as a very effective personal feature. In this paper, we propose a novel quantization optimization method for energy histogram according to the maximum entropy principle (MEP) as a design criterion. Publicly available AT&T database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using optimized energy histogram by maximization of information-theoretic entropy can achieve much higher recognition rate. Index Terms-Face recognition, binary vector quantizaiton (BVQ), energy histogram, DCT coefficients. Manuscript University. Since 1998, he has been a professor at New Industry Creation Hatchery Center (NICHe), Tohoku University. His research field covers whole Si-based semiconductor and flat panel display technologies in terms of material, process, device, circuit, and system technologies. He is known as an originator of "Ultra1clean Technology," which introduced ultraclean and scientific way of thinking into semiconductor manufacturing industry and became indispensable technology today.
doi:10.7763/ijmlc.2015.v5.542 fatcat:r7vbfwxwpzakvd6ektrpgefb7y