An Approach: Modality Reduction and Face-Sketch Recognition

TL;DR

This paper proposes a novel face sketch recognition method that reduces modality differences using wavelet transforms and PCA, improving classification accuracy with SVM and K-NN classifiers.

Contribution

It introduces a modality reduction approach combining wavelet transform and PCA for face sketch recognition, validated on CUHK datasets.

Findings

Robust against good lighting and frontal pose images

Effective modality reduction improves recognition accuracy

Validated on CUHK face and sketch datasets

Abstract

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.