• Journal of KIISE
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• v.44 no.3
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• pp.295-305
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• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.885-893
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• 2003

In this paper, a new practical implementation of a person verification system using features of longitudinal section and transection and other facial, rotation compensated 3D face image, is proposed. The approach works by finding the nose tip that has a protrusion shape on the face. In feature recognition of 3D face image, one has to take into consideration the orientated frontal posture to normalize. Next, the special points in regions, such as nose, eyes and mouth are detected. The depth of nose, the area of nose and the volume of nose based both on the 3 longitudinal section and a transection are calculated. The eye interval and mouth width are also computed. Finally, the 12 features on the face were extracted. The Ll measure for comparing two feature vectors were used, because it is simple and robust. In the experimental results, proposed method achieves recognition rate of 95.5% for the longitudinal section and transection.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.34-40
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• 2006

1. Objectives We are going to develope 3D Face Recognition Apparatus to analyse the facial characteristics of the Sasangin. In the process, we should identify the recognition rate of the three dimensional position using this Apparatus. 2. Methods We took a photograph of calibrator($280{\times}400mm$) with interval of 20mm longitudinal direction of 10 times using 3D Face Recognition Apparatus. In the practice, we obtained 967 point to the exclusion of points deviating from the visual field of dual camera. And we made a comparison between measurement values and three dimensional standard values to calculate the errors. 3. Results and Conclusions In this test, the average error rate of X axis values was 0.019% and the maximum error rate of X axis values was 0.033%, the average error rate of Y axis values was 0.025% and the maximum error rate of Y axis values was 0.044%, the average error rate of Z axis values was 0.158% and the maximum error rate of Z axis values was 0.269%. This results exhibit much improvement upon the average error rate 1% and the maximum error rate 2.242% of the existing 3D Recognition Apparatus. In conclusion, we assessed that this apparatus was adaptable to abstract the facial characteristic point from three dimensional face shape in the mechanical aspects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.63-69
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• 2004

This paper proposes a novel method for achieving pose-invariant face recognition using cylindrical model. On the assumption that a face is shaped like that of a cylinder, we estimate the object's pose and then extract the frontal face image via a pose transform with previously estimated pose angle. By employing the proposed pose transform technique we can increase the face recognition performance using the frontal face images. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the pose transform. Additionally, the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.35-47
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• 2018

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.59-68
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• 1999

Recently, research and developmental direction of communication system is concurrent adopting voice data and face image in speaking to provide more higher recognition rate then in the case of only voice data. Therefore, we present a method of lipreading in speech image sequence by using the 3-D facial shape model. The method use a feature information of the face image such as the opening-level of lip, the movement of jaw, and the projection height of lip. At first, we adjust the 3-D face model to speeching face image sequence. Then, to get a feature information we compute variance quantity from adjusted 3-D shape model of image sequence and use the variance quality of the adjusted 3-D model as recognition parameters. We use the intensity inclination values which obtaining from the variance in 3-D feature points as the separation of recognition units from the sequential image. After then, we use discrete HMM algorithm at recognition process, depending on multiple observation sequence which considers the variance of 3-D feature point fully. As a result of recognition experiment with the 8 Korean vowels and 2 Korean consonants, we have about 80% of recognition rate for the plosives and vowels. We propose that usability with visual distinguishing factor that using feature vector because as a result of recognition experiment for recognition parameter with the 10 korean vowels, obtaining high recognition rate.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.423-424
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• 2006

This paper describes a 3D face recognition using different devices for 3D faces and input faces which include several different pose. Before the recognition stage, through the EC-SVD, all data have to be preprocessed and normalized. At recognition stage, we propose the multi-point signature method for measuring facial surface information. And we use the root mean square error for matching. From the experiment results, we have 92.5% recognition rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.