• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.112-120
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• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1310-1319
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• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.53-58
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• 2000

This paper reconstructs local region of a facial expression image from extracted feature points of facial expression image using FCM(Fuzzy C-Meang) clustering algorithm with Gabor wavelets. The feature extraction in a face is two steps. In the first step, we accomplish the edge extraction of main components of face using average value of 2-D Gabor wavelets coefficient histogram of image and in the next step, extract final feature points from the extracted edge information using FCM clustering algorithm. This study presents that the principal components of facial expression images can be reconstructed with only a few feature points extracted from FCM clustering algorithm. It can also be applied to objects recognition as well as facial expressions recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.4
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• pp.221-226
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• 2005

This paper presents a hybrid method for recognizing the faces by using zero mean and principal component analysis. Zero mean is applied to reduce the 1st order statistics to data nonlinearities. PCA is also used to derive an orthonormal basis which directly leads to dimensionality reduction, and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 20 face images(10 persons * 2 scenes) of 324*243 pixels from Yale face database. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.153-156
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• 2007

In this paper, we propose a fast face detection approach using PCA and SVM. In our detection system, first we filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, we use SVM classifier to detect whether there are faces present in the test image. Support Vector Machine (SVM) has great performance in classification task. PCA is used for dimension reduction of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.133-139
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• 2008

Face recognition technologies using PCA(principal component analysis) recognize faces by deciding representative features of faces in the model image, extracting feature vectors from faces in a image and measuring the distance between them and face representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of face models of the same class is used as recognition unit for the images inputted on a continual input image. This paper proposes a new PCA recognition in which database of faces.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.11-17
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• 2010

In this parer, face recognition method which can be applied to ITCS (interactive TV control system) is proposed. We extracted ULBP(uniform local binary pattern) histogram feature from infra-red images, and we detected left-right eyes and face region by using SVM classifier. Then, We implemented face recognition system which is using Gabor transform and ULBP histogram feature and applied to personal verification for ITCS.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.83-90
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• 2010

Cancelable biometrics is a robust and secure biometric recognition method using revocable biometric template in order to prevent possible compromisation of the original biometric data. In this paper, we present a new cancelable bits extraction method for the facial data. We use our previous cancelable feature template for the bits extraction. The adopted cancelable template is generated from two different original face feature vectors extracted from two different appearance-based approaches. Each element of feature vectors is re-ordered, and the scrambled features are added. With the added feature, biometric bits string is extracted using helper data based method. In this technique, helper data is generated using statistical property of the added feature vector, which can be easily replaced with straightforward revocation. Because, the helper data only utilizes partial information of the added feature, our proposed method is a more secure method than our previous one. The proposed method utilizes the helper data to reduce feature variance within the same individual and increase the distinctiveness of bit strings of different individuals for good recognition performance. For a security evaluation of our proposed method, a scenario in which the system is compromised by an adversary is also considered. In our experiments, we analyze the proposed method with respect to performance and security using the extended YALEB face database