I.J.Modern Education and Computer Science, 2012, 1, 12-18 Published Online February 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2012.01.02 Copyright © 2012 MECS I.J. Modern Education and Computer Science, 2012, 1, 12-18 Perceived Gender Classification from Face Images Hlaing Htake Khaung Tin University of Computer Studies, Yangon, Myanmar hlainghtakekhaungtin@gmail.com Abstract—Perceiving human faces and modeling the distinctive features of human faces that contribute most towards face recognition are some of the challenges faced by computer vision and psychophysics researchers. There are many methods have been proposed in the literature for the facial features and gender classification. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. The features set is applied to three different applications: face recognition, facial expressions recognition and gender classification, which produced the reasonable results in all database. In this paper described two phases such as feature extraction phase and classification phase. The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also real- time capable and automatic. Index Terms—Face Recognition, Facial Expression, Gender Classification, Feature Extraction, Eigen faces. I. INTRODUCTION In the last several years, various feature extraction and pattern classification methods have been developed for gender classification. Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource limited algorithms. Perceived gender classification is a research topic with a high application potential in areas such as surveillance, face recognition, video indexing, and dynamic marketing surveys. Moghaddam and Yang [1] introduced the best gender recognition algorithm in terms of reported classification rate. They adopted an appearance-based approach with a classifier based on a Support Vector Machine with Radial Basis Function Kernel (SVM+RBF)[1]. They reported a 96.6 percent recognition rate for classifying 1,775 images from the FERET database using automatically aligned and cropped images and a fivefold cross validation. Previous simulations by Fleming and Cottrell [2], using masking of bottom and top face areas, with a more computationally demanding nonlinear approach, were not strikingly accurate for sex classification under either masking condition, but showed better performance on the top portion of the face. When the top of the face was masked the model was 29% correct, and when the bottom of the face was masked the model was 55% correct. However, this relatively low performance could be attributed, at least partially, to the great variation of training stimuli, which included non-face images. One possible reason for the difference found between top and bottom conditions is that the Fleming and Cottrell stimuli included the hair and thus provided more variation in the lower portion of the image, particularly for female faces. Previous studies of facial area contribution to sex classification by human subjects from photographic images have used several approaches: presenting features (or a combination of features) in isolation [3,4] masking features [5,4] and replacing features within a full image [3,6]. In some cases, the studies have used individual photographic images, and in other cases, male and female prototypes have been created using various averaging techniques. These studies have produced varying results. Differences obtained between tests of features in isolation and substitution of features have been attributed to the role of configuration in facial tasks [3,4]. For example, although the nose alone provides little information, masking it diminishes the total amount of configural information perceived. In general, these studies indicate that the isolated areas contributing the most to sex classification are: the eye region (particularly the eyebrows), and the face outline (particularly the jaw). Human facial image processing has been an active and interesting research issue for years. Since human faces provide a lot of information, many topics have drawn lots of attentions and thus have been studied intensively. The most of these is face recognition [7]. Other research topics include predicting feature faces [8] reconstructing faces from some prescribed features [9]. Gender classification is important visual tasks for human beings, such as many social interactions critically depend on the correct gender perception. As visual surveillance and human-computer interaction technologies evolve, computer vision systems for gender classification will play an increasing important role in our lives [10]. Gender classification is arguably one of the more important visual tasks for an extremely social animal like us humans many social interactions critically depend on the correct gender perception of the parties involved. Arguably, visual information from human faces provides one of the more important sources of information for gender classification. Not surprisingly, thus, that a very large number of psychophysical studies has investigated gender classification from face perception in humans [11]. The usual assumptions (behind inductive learning) may not hold for many applications. For example, if the input values of the test samples are known (given), then an appropriate goal of learning may be to predict outputs