An Empirical Investigation on the Effect of Shape Exaggeration in Face Sketch to Photo Matching Facial sketch to photo retrieval system has useful application in forensic investigation. The main problem identified in the system is that the sketch and photo are from different modality. In order to address this problem, intramodality and intermodality matching approaches have been introduced by many researchers. However, the proposed methods mostly ignore the effects of shape exaggeration of the facial sketch. In this paper, we attempt to investigate empirically and highlight the importance of considering the shape exaggeration effects in any of the two aforementioned matching approaches. This investigation is limited to the Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets. The results demonstrate that the shape exaggeration effects may reduce retrieval rate accuracy.

An Improved Siamese Network for Face Sketch Recognition Face sketch recognition identifies the face photo from a large face sketch dataset. Some traditional methods are typically used to reduce the modality gap between face photos and sketches and gain excellent recognition rate based on a pseudo image which is synthesized using the corresponded face photo. However, these methods cannot obtain better high recognition rate for all face sketch datasets, because the use of extracted features cannot lead to the elimination of the effect of different modalitiesu0027 images. The feature representation of the deep convolutional neural networks as a feasible approach for identification involves wider applications than other methods. It is adapted to extract the features which eliminate the difference between face photos and sketches. The recognition rate is high for neural networks constructed by learning optimal local features, even if the input image shows geometric distortions. However, the case of overfitting leads to the unsatisfactory performance of deep learning methods on face sketch recognition tasks. Also, the sketch images are too simple to be used for extracting effective features. This paper aims to increase the matching rate using the Siamese convolution network architecture. The framework is used to extract useful features from each image pair to reduce the modality gap. Moreover, data augmentation is used to avoid overfitting. We explore the performance of three loss functions and compare the similarity between each image pair. The experimental results show that our framework is adequate for a composite sketch dataset. In addition, it reduces the influence of overfitting by using data augmentation and modifying the network structure.