Face sketch recognition using local invariant features

Face sketch recognition is one of the recent biometrics, which is used to identify criminals. In this paper, a proposed model is used to identify face sketch images based on local invariant features. In this model, two local invariant feature extraction methods, namely, Scale Invariant Feature Transform (SIFT) and Local Binary Patterns (LBP) are used to extract local features from photos and sketches. Minimum distance and Support Vector Machine (SVM) classifiers are used to match the features of an unknown sketch with photos. Due to high dimensional features, Direct Linear Discriminant Analysis (Direct-LDA) is used. CHUK face sketch database images is used in our experiments. The experimental results show that SIFT method is robust and it extracts discriminative features than LBP. Moreover, different parameters of SIFT and LBP are discussed and tuned to extract robust and discriminative features.