SUPPORT VECTOR LEARNING FOR GENDER CLASSIFICATION USING AUDIO AND VISUAL CUES

Abstract

Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector Machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful conclusion.