Development of a Modified Local Binary Pattern-Gabor Wavelet Transform Aging Invariant Face Recognition System

Human faces undergo considerable amount of variations with aging. This variation being experienced in facial texture and shape with different ages of a particular subject makes recognition of faces very difficult. However, most existing Face Recognition Systems (FRS) suffer from high misclassification of faces because of the large variation in face appearances of the same individual due to aging. This drawback is also aggravated by the fact that most currently existing age-invariant FRS adopt holistic feature extraction techniques (FET), which are computationally timeinefficient and suffer from the curse of dimensionality, in their development. Sequel to these, a swarm-optimized age-invariant FET for a FRS was developed and presented in this paper. The developed swarm-optimized age-invariant FET tagged swarmoptimized LBP-GWT, which consists of Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT), was used for extraction of facial features from the face images. Procedurally, LBP and GWT were used to extract facial features relating to the eye lids, nose and lips. Discriminant features were selected from the features extracted by LBP and GWT using particle swarm optimization algorithm. The selected features were fused into a single feature set using sum rule strategy. Based on the single feature set, faces were recognized and classified into age-varying collections of different individuals using support vector machine. The developed swarm-optimized age invariant feature extraction technique serves as improvement over Histogram of Gradients, Principal Component Analysis-Local Discriminant Analysis, Local Binary Pattern and Gabor Wavelet Transform feature extraction techniques in terms of false accept rate, false reject rate, recognition accuracy and recognition time. This technique could be integrated into emerging age-invariant face recognition systems towards their improved performance.