Local Gradient Increasing Pattern (LGIP) for Facial Representation and Gender Recognition
A robust facial representation is an essential component for gender classification. This paper introduces a new local feature, Local Gradient Increasing Pattern (LGIP), which expresses the local intensity increasing trend. A LGIP feature is to encode intensity increasing trends in 8 orientations at each pixel using signs of directional gradient responses, and overall increasing trend is assigned with a decimal label. A facial image is partitioned into overlapping regions from which LGIP histograms are obtained and concatenated into a single feature vector. Gender classification is carried out using SVM classifier based on the LGIP-based facial descriptor. We investigate the influence to recognition rates by two factors, image resolution and person-dependent/independent condition. Experiments are performed on two replicable image sets from CAS-PEAL and FERET databases, and the results show that our method achieves better performance than many other methods.
Keywordsgender classification local gradient increasing pattern facial representation support vector machine
Unable to display preview. Download preview PDF.
- 3.Gutta, S., Wechsler, H., Phillips, P.J.: Gender and ethnic classification of face images. In: 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 194–199 (1998)Google Scholar
- 4.Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Tech. rep. (2003), http://www.csie.ntu.edu.tw/cjlin/pa-pers/guide/guide.pdf
- 5.Jabid, T., Hasanul Kabir, M., Chae, O.: Gender classification using local directional pattern (ldp). In: 20th International Conference on Pattern Recognition (ICPR), pp. 2162–2165 (2010)Google Scholar