Special Issue Article Open Access

Unconstrained Face Recognition Using SVM Across Blurred And Illuminated Images With Pose Variation

Abstract

Although face recognition has been actively studied over the past decade, still recognizing faces appearing in unconstrained, natural conditions remains a challenging task. Recently, researchers have begun to investigate face recognition under unconstrained conditions that is referred to as unconstrained face recognition The state-of-the-art recognition systems yield satisfactory performance only under controlled scenarios like photographs taken under controlled light, background etc. Recognition accuracy degrades significantly when confronted with unconstrained situations. The main factors that make this problem challenging are image degradation due to blur and appearance variations due to illumination and pose. In this project the problem of blur, pose and illumination are jointly addressed. Here a Robust recognition of Blurred faces is proposed which assumes a symmetric constraint for the blur depending on several types of blur and visual features can be optimally integrated. Then faces under different pose have been recognized by normalizing it using affine transformation. Here an input face image is normalized to frontal view using the irises information. Use affine transformation parameters to align the input pose image to frontal view. After completing the aforementioned pose normalization process, the resulting final image undergoes illumination normalization. This is performed using the SQI algorithm. Finally Support vector machine classifier is adapted to uniquely identifying facial characteristics by classifying the face feature in training and testing set.

Nadeena M, S.Sangeetha, M.E

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