• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.129-136
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• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.87-95
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• 2006

In this paper, we propose a novel feature extraction method for face recognition, based on Discrete Cosine Transform (DCT), Energy Probability (EP), and Linear Discriminant Analysis (LDA). We define an energy probability as magnitude of effective information and it is used to create a frequency mask in OCT domain. The feature extraction method consists of three steps; i) the spatial domain of face images is transformed into the frequency domain called OCT domain; ii) energy property is applied on DCT domain that acquire from face image for the purpose of dimension reduction of data and optimization of valid information; iii) in order to obtain the most significant and invariant feature of face images, LDA is applied to the data extracted using frequency mask. In experiments, the recognition rate is 96.8% in ETRI database and 100% in ORL database. The proposed method has been shown improvements on the dimension reduction of feature space and the face recognition over the previously proposed methods.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.758-768
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• 2017

In this paper, we present a novel method to extract face representation based on multi-resolution spatial pyramid. In our method, a face is subdivided into increasingly finer sub-regions (local regions) and represented at multiple levels of histogram representations. To cope with misaligned problem, patch-based local descriptor extraction has been also developed in a novel way. To preserve multiple levels of detail in local characteristics and also encode holistic spatial configuration, histograms from all levels of spatial pyramid are integrated by using dimensionality reduction and feature combination, leading to our spatial-pyramid face feature representation. We incorporate our proposed face features into general face recognition pipeline and achieve state-of-the-art results on challenging face recognition problems.

• 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

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.10-10
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• 2003

This paper extracts the edge of main components of face with Gabor wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.656-659
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• 2001

Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.349-355
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• 2001

Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2496-2511
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• 2014

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.