Comparative evaluation of age classification from facial images

Researchers have made efforts to achieve age classification using spatial and transform domain techniques with various classifiers. Spatial Domain techniques are based on human perception and susceptible to noise and image processing operations. Transform domain techniques provide high flexibility and robustness in selection of features and better classification efficiency. This paper uses transform domain feature extraction techniques to achieve maximum possible age classification efficiency. The transforms used in this paper to extract features are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Dual Tree Complex Wavelet Transform (DTCWT). The features extracted from facial images are classified into a range of age groups viz. child, adolescent, young, middle aged and old aged using variance, k-nearest neighbour (kNN) and hybrid variance as classifiers. The experimental results prove that the feature extraction using DTCWT with Hybrid variance classifiers provides better classification efficiency than that of DCT and DWT.