Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

Online RGB-D gesture recognition with extreme learning machines

Abstract: Gesture recognition is needed in many applications such as human-computer interaction and sign language recognition. The challenges of building an actual recognition system do not lie only in reaching an acceptable recognition accuracy but also with requirements for fast online processing. In this paper, we propose a method for online gesture recognition using RGB-D data from a Kinect sensor. Frame-level features are extracted from RGB frames and the skeletal model obtained from the depth data, and then classified by multiple extreme learning machines. The outputs from the classifiers are aggregated to provide the final classification results for the gestures. We test our method on the ChaLearn multi-modal gesture challenge data. The results of the experiments demonstrate that the method can perform effective multi-class gesture recognition in real-time.

Illumination Tolerant Face Recognition Using a Novel Face From Sketch Synthesis Approach and Advanced Correlation Filters

Abstract: Current state-of-the-art approach for performing face sketch recognition transforms all the test face images into sketches, and then performs recognition on sketch domain using the sketch composite. In our approach we propose the opposite; which has advantages in a real-time sysrtem; we propose to generate a realistic face image from the composite sketch using a Hybrid subspace method and then build an illumination tolerant correlation filter which can recognize the person under different illumination variations from a surveillance video footage. We show how effective proposed algorithm works on the CMU PIE (Pose Illumination and Expression) database.

Face sketch recognition by Local Radon Binary Pattern: LRBP

Abstract: In this paper, we propose a new face descriptor to directly match face photos and sketches of different modalities, called Local Radon Binary Pattern (LRBP). LRBP is inspired by the fact that the shape of a face photo and its corresponding sketch is similar, even when the sketch is exaggerated by an artist. Therefore, the shape of face can be exploited to compute features which are robust against modality differences between face photo and sketch. In LRBP framework, the characteristics of face shape are captured by transforming face image into Radon space. Then, micro-information of face shape in new space is encoded by Local Binary Pattern (LBP). Finally, LRBP is computed by concatenating histograms of local LBPs. In order to capture both local and global characteristics of face shape, LRBP is extracted in a spatial pyramid fashion. Experiments on CUFS and CUFSF datasets indicate the efficiency of LRBP for face sketch recognition.

Pattern Recognition Technologies and Applications: Recent Advances

Abstract: The nature of handwriting in our society has significantly altered over the ages due to the introduction of new technologies such as computers and the World Wide Web. With increases in the amount of signature verification needs, state of the art internet and paper-based automated recognition methods are necessary. "Pattern Recognition Technologies and Applications: Recent Advances" provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification.