Recognition Rate Of Images Research Articles

An improved face recognition technique based on modular PCA approach

A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary even when the pose, lighting direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standard face databases.

Parallel‐hierarchical neural network for 3D object recognition

AbstractIn this paper, the authors propose a parallel‐hierarchical neural network that can recognize multiple 3D objects from 2D projection images. The proposed network focuses on a parallel‐hierarchical structure and memory‐based recognition assistance, which are characteristics of the excellent vision systems that living organisms have, and refers to the neocognitron, which models the parallel‐hierarchical structure of vision systems. The amount of calculations is reduced by deleting cells having a low degree of importance based on competition between cells and detecting features that differ for each cell. The network not only can recognize an object, but can also estimate its orientation at the same time. The memory‐based recognition assistance is modeled by performing iterative processing during which the input image approaches a learning image based on the orientation estimation result. Weights are determined by sequentially presenting learning images, and no teaching signal is necessary. This is done in a short time since it is not an iterative learning method. The network performance was evaluated by using five objects from COIL‐100. Images that were obtained by photographing each object after it was rotated 60° at a time around the vertical axis were used for the learning images. The experiments checked the recognition rates for various 2D projection images that were obtained from the 3D objects. These results verified the effectiveness of the proposed neural network as a 3D object recognition technique. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 35(1): 1–12, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10579

영상과 음성의 출력 데이터를 이용한 감성 인식

In this paper, we propose a method for recognizing the human s emotion using output data of image and speech. The proposed method is based on the recognition rate of image and speech. In case that we use one data of image or speech, it is hard to produce the correct result by wrong recognition. To solve this problem, we propose the new method that can reduce the result of the wrong recognition by multiplying the emotion status with the higher recognition rate by the higher weight value. To experiment the proposed method, we suggest the simple recognizing method by using image and speech. Finally, we have shown the potentialities through the expriment.

Hand gesture recognition using combined features of location, angle and velocity

The use of hand gesture provides an attractive alternative to cumbersome interface devices for human–computer interaction (HCI). Many hand gesture recognition methods using visual analysis have been proposed: syntactical analysis, neural networks, the hidden Markov model (HMM). In our research, an HMM is proposed for various types of hand gesture recognition. In the preprocessing stage, this approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects hand candidate regions on the basis of skin-color and motion. The hand tracking algorithm finds the centroids of the moving hand regions, connects them, and produces a hand trajectory. The gesture spotting algorithm divides the trajectory into real and meaningless segments. To construct a feature database, this approach uses a combined and weighted location, angle and velocity feature codes, and employs a k-means clustering algorithm for the HMM codebook. In our experiments, 2400 trained gestures and 2400 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a satisfactory and higher recognition rate for user images of different hand size, shape and skew angle.