Transfer Learning for Cross-Dataset Recognition: A Survey <s> face recognition <s> A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> face recognition <s> In various computer vision applications, often we need to convert an image in one style into another style for better visualization, interpretation and recognition; for examples, up-convert a low resolution image to a high resolution one, and convert a face sketch into a photo for matching, etc. A semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned. The dictionary pair can well characterize the structural domains of the two styles of images, while the mapping function can reveal the intrinsic relationship between the two styles' domains. In SCDL, the two dictionaries will not be fully coupled, and hence much flexibility can be given to the mapping function for an accurate conversion across styles. Moreover, clustering and image nonlocal redundancy are introduced to enhance the robustness of SCDL. The proposed SCDL model is applied to image super-resolution and photo-sketch synthesis, and the experimental results validated its generality and effectiveness in cross-style image synthesis. <s> BIB002
The dataset shift in face recognition can be caused by poses, resolution, illuminations, expressions, and modality. For example, MultiPIE dataset 15 BIB001 contains face images under various poses, illuminations and expressions. The face images under one pose can be used as the source dataset while the images under another pose can be used as the target dataset. Similarly, the illuminations and expressions can also cause the distribution shift, and can be used for evaluating cross-domain performance. Some face datasets also contain face images captured by different sensors, resulting in multiple data modalities. For instance, the Oulu-CASIA NIR&VIS facial expression database 16 and the BUAA-VisNir Face Database 17 contain face images captured by both NIR (Near Infrared) and VIS (Visible light). There are also datasets provide the sketch of human faces, such as Face Sketch Database (CUFS) 18 . The recognition across photo and sketch has also been addressed BIB002 , which has the potential application of law enforcement. For example, it is often required to compare the face photos to a sketch drawn by an artist based on the verbal description of the suspect.