Curvelet Based Feature Extraction

Designing a completely automatic and efficient face recognition system is a grand challenge for biometrics, computer vision and pattern recognition researchers. Generally, such a recognition system is able to perform three subtasks: face detection, feature extraction and classification. We’ll put our focus on feature extraction, the crucial step prior to classification. The key issue here is to construct a representative feature set that can enhance system-performance both in terms of accuracy and speed. At the core of machine recognition of human faces is the extraction of proper features. Direct use of pixel values as features is not possible due to huge dimensionality of the faces. Traditionally, Principal Component Analysis (PCA) is employed to obtain a lower dimensional representation of the data in the standard eigenface based methods [Turk and Pentland 1991]. Though this approach is useful, it suffers from high computational load and fails to well-reflect the correlation of facial features. The modern trend is to perform multiresolution analysis of images. This way, several problems like, deformation of images due to in-plane rotation, illumination variation and expression changes can be handled with less difficulty. Multiresolution ideas have been widely used in the field of face recognition. The most popular multiresolution analysis tool is the Wavelet Transform. In wavelet analysis an image is usually decomposed at different scales and orientations using a wavelet basis vector. Thereafter, the component corresponding to maximum variance is subjected to ‘further operation’. Often this ‘further operation’ includes some dimension reduction before feeding the coefficients to classifiers like Support Vector Machine (SVM), Neural Network (NN) and Nearest Neighbor. This way, a compact representation of the facial images can be achieved and the effect of variable facial appearances on the classification systems can also be reduced. The wide-spread popularity of wavelets has stirred researchers’ interest in multiresolution and harmonic analysis. Following the success of wavelets, a series of multiresolution, multidimensional tools, namely contourlet, curvelet, ridgelet have been developed in the past few years. In this chapter, we’ll concentrate on Digital Curvelet Transform. First, the theory of curvelet transform will be discussed in brief. Then we'll talk about the potential of curvelets as a feature descriptor, looking particularly into the problem of image-based face recognition. Some experimental results from recent scientific works will be provided for ready reference. 3