Improved Face Recognition using Image Resolution Reduction and Optimization of Feature Vector

Abstract

Face recognition is a difficult problem that involves automated matching of a given face image with corresponding person’s image(s) in a database. Face recognition finds application in areas like surveillance & security, digital libraries and human computer interactions. Successful, speedy and practically feasible face recognition method depends heavily on the choice of feature vector used for classification and addressing the curse of image dimension. The dimension reduction and the skill to acquire minimum size of feature vector required for face recognition for diverse facial expressions is a challenging task in face recognition. Dimension reduction results in removal of irrelevant variables alongwith noise therein and a lower computation complexity of subsequent processing. This dissertation addresses the challenges of dimension reduction, choice of minimum size feature vector for face recognition and minimization of adverse effects of varying facial expressions on the recognition through reduction in image resolution. In preprocessing of face images, scale normalization is carried out through a novel scale normalization algorithm to retain only the facial part of images. This helps in reducing computational complexity by restricting dimensions of image to face region only. Tilt of face images is removed by calculating the gradient between the two eyes and applying the reverse rotation. The issue of dimensionality is addressed first by gradually reducing image resolution through spatial domain low pass filtering followed by decimation. The second method involves novel coefficient selection strategies to choose the minimum dimension of feature vector required for recognition with maximum recognition rate and reduced computational complexity. Face images with varying image resolution are obtained by varying the decimation factor. The effects of variation in image resolution on face recognition have been evaluated using template matching and Principle Components Analysis (PCA) based face recognition techniques. Classical PCA technique has been modified into sub-holistic PCA. Better recognition rate is achieved using modified PCA method with reduced image resolution. Improved recognition rate results are reported using novel coefficients selection and optimization methods in Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete wavelets Transform (DWT) based face recognition methods. The experiments are carried out for various image resolutions using five different datasets. Improved recognition rate of 97.2% (template matching), 87% (PCA), 94% (Sub-holistic PCA), 100% (DFT), 95.75% (DCT) and 99.25% (DWT) is achieved at a specific image resolution for different datasets. The resolution reduction method used with square images is then extended to hexagonal images. A new technique based on Diagonal grow and Butterfly structure methodology has been developed for sampling and indexing hexagonal structure in hexagonal image processing frame work. Proposed strategy offer less pixel redundancy as compared to existing techniques. Reduction in pixel redundancy varies according to size of square image.