Robust face pseudo-sketch synthesis and recognition using morphological-arithmetic operations and HOG-PCA

In this paper, we propose a simple but yet effective method for synthesizing a pseudo face sketch (pseudo-sketch) from a photo, to be used for face recognition based on sketches drawn by a forensic artist. In contrast to current methods, the proposed method does not require training samples while fairly maintains the salient facial features as the artist do. We also propose a matching method on the basis of the Histograms of Oriented Gradients (HOG) descriptor and Principal Component Analysis (PCA), called HOG-PCA, to handle the similarities between a forensic sketch and a synthesized pseudo-sketch. In this method, we first extract the HOG features for the sketch and pseudo-sketch at regular grid and overlapped patches. The PCA is then applied to address the redundancy in feature representation due to several overlapped patches. Finally, the Nearest Neighbors Classifier (NNC) with the cosine distance is used to classify the sketch and pseudo-sketch pairs as matched or mismatched. Experimental results on CUHK and AR face sketch databases demonstrate that our proposed methods outperform state-of-the-art methods.