An Approach: Modality Reduction and Face-Sketch Recognition
This addresses a specific challenge in face recognition for security or forensic applications, but it is incremental as it builds on existing methods like wavelet transforms and PCA.
The paper tackles the problem of recognizing face sketches from face photo databases by reducing modality differences using a 2D Discrete Haar wavelet transform and PCA for feature extraction, achieving experimental verification with 100 training and 100 testing images under good lighting and frontal pose conditions.
To recognize face sketch through face photo database is a challenging task for todays researchers. Because face photo images in training set and face sketch images in testing set have different modality. Difference between two face photos of difference person is smaller than the difference between same person in a face photo and face sketched. In this paper, for reduction of the modality between face photo and face sketch we first bring face photo and face sketch images in a new dimension using 2D Discrete Haar wavelet transform with scale 3 followed by a negative approach. After that, extract features from transformed images using Principal Component Analysis (PCA). Thereafter, we use SVM classifier and K-NN classifier for better classification. Our proposed method is experimentally verified by its robustness against faces that are captured in a good lighting condition and in a frontal pose. The experiment has been conducted with 100 male and female face images as training set and 100 male and female face sketch images as testing set collected from CUHK training and testing cropped photos and CUHK training and testing cropped sketches.