Sketch recognition

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

Non-rigid shape recognition for sign language understanding

Abstract: The recognition of human activities from video sequences is currently one of the most active areas of research because of its many applications in video surveillance, multimedia communications, medical diagnosis, forensic research and sign language recognition. The work described in this paper describes a new method designed to precisely identify human gestures for Sign Language recognition. The system is to be developed and implemented on a standard personal computer (PC) connected to a colour video camera. The present paper tackles the problem of shape recognition for deformable objects like human hands using modern classification techniques derived from artificial intelligence.

Self-Similarity Descriptor and Local Descriptor-Based Composite Sketch Matching

Abstract: Composite sketching belongs to the forensic science where the sketches are drawn using freely available composite sketch generator tools. Compared to pencil sketches, composite sketches are more effective because it consumes less time. It can be easily adopted by people across different regions; moreover, it does not require any skilled artist for drawing the suspects faces. Software tool used to generate the faces provides more features which can be used by the eyewitness to provide better description, which increases the clarity of the sketches. Even the minute details of the eyewitness description can be captured with great accuracy, which is mostly impossible in pencil sketches. Now that a composite sketch is provided, it has to be identified effectively. In this paper we have analyzed two state-of-the-art techniques for composite sketch image recognition: Self-similarity descriptor (SSD)-based composite sketch recognition and local descriptors (LD)-based composite sketch recognition. SSD is mainly used for developing a SSD dictionary-based feature extraction and Gentle Boost KO classifier-based composite sketch to digital face image matching algorithm. LD is mainly used for multiscale patch-based feature extraction and boosting approach for matching composites with digital images. These two techniques are validated on FACES and IdentiKit databases. From our analysis we have found that SSD descriptor works better than LD. Using SSD method we obtained the results for FACES (ca) as 51.9 which is greater when compared to LD which gives a result of 45.8. Similarly, using SSD, values of 42.6 and 45.3 for FACES (As) and IdentiKit (As), respectively, are obtained which are much better than the values of 20.2 and 33.7 for FACES (As) and IdentiKit (As), respectively, using LD method.

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques.

Abstract: Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification ave evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.

A reservoir computing approach for human gesture recognition from kinect data

Abstract: This paper describes a novel approach for human gesture recognition from motion data captured by a Kinect camera. The proposed method is based on encoding the temporal history of input data using bidirectional Echo State Networks, whereas the output is computed by means of a multi-layer perceptron with softmax. Results achieved at the time-series classification challenge organized within the 2016 ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data show the potentiality of the approach.