Face Sketch Recognition Using Local Invariant Features

Alaa Tharwat, Hani Mahdi, A. E. Hassanien, and Adel El Hennawy, "Face Sketch Recognition Using Local Invariant Features", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015.

Date Presented:

November 13 - 15


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.