We propose a novel information-processing algorithm called Vector Quantization (VQ) codebook space information processing. Based on this algorithm, we have developed a very simple yet highly reliable face recognition method called VQ histogram method. General codebook consisting of 33 low-frequency patterns for VQ processing is created by theoretical method. codevector referred (or matched) count histogram, which is obtained by VQ processing of facial image, is utilized as a very effective personal feature value. By applying appropriate low pass filtering to facial image and VQ processing, useful features for face recognition can be extracted. Experimental results show recognition rate of 95.6 %for 40 persons’ 400 images of publicly available AT&T database containing variations in lighting, posing, and expressions. By combining multiple low pass filtering procedures, recognition rate increases up to 97 % or higher. After considering the essence of the VQ histogram method, moreover, we also have developed another face recognition method called Adjacent Pizel Intensity Difference Quantization (APIDQ) Histogram Method, which is simpler and more efficient in feature extraction than the former. Experimental results show an equivalent recognition rate of 95.7 % to VQ histogram method. By utilizing the table look-up (TLU) method in the quantization step, the total recognition processing time is reduced to only 31 msec.
- Adjacent pixel intensity difference quantization (APIDQ)
- Histogram method, Face recognition
- Vector quantization (VQ)
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence