• 조명전기설비학회논문지
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• 제28권4호
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• pp.70-79
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• 2014

In this paper, I propose a method for detection of look-alike offenders by using hidden face information. For extraction of moving objects, PRA matching is used to extract moving components, and brightness changes can be dealt with by an adaptive threshold adjusting in the proposed method. Moving objects extracted in the territory of the face region is extracted using the complexion, facial area, eyes, nose, mouth. The extracted information detected by the presence of these characteristics were likely to help judge a person. Results of the extracted face makes the recognition rate of possible murderers 90% so the usefulness of the proposed method was confirmed.

• 한국정보전자통신기술학회논문지
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• 제4권1호
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• pp.3-8
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• 2011

본 논문은 실험영상이 학습영상에 대해 조명의 차이가 있는 경우에도 데이터베이스 안에서 누구인지를 식별하는 얼굴인식 방법을 제안하였으며, 또한 HMM과 KLT를 이용한 얼굴인식 알고리즘의 수행결과를 비교, 분석하였다. 얼굴인식 방법으로 측정벡터는 직교변환(Karhuman Loevs Trans-form : KLT)의 상관관계를 이용하여 얻은 HMM의 정역학특성을 사용하여 HMM 기존의 얼굴인식 방법에서 인식률을 개선하였으며, 실험결과로써 조명의 조건에 따른 여러 가지 복잡한 주변 상황변화에서도 제안된 방식의 효율성을 입증할 수 있었다.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1075-1086
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• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2036-2039
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• 2003

This paper focuses on the study of the robustness of face authentication methods under illumination changes. Four different face authentication methods are tried. These methods are as follows; Principal Component Analysis, Gaussian Mixture Models, 1-Dimensional Hidden Markov Models, 2-Dimensional Hidden Markov Models. Experiment results involving an artificial illumination change to face images are compared with each others. Face feature vector extraction method based on the 2-Dimensional Discrete Cosine Transform is used. Experiments to evaluate the above four different face authentication methods are carried out on the Olivetti Research Laboratory(ORL) face database. For the pseudo 2D HMM, the best EER (Equal Error Rate) performance is observed.

• 전자공학회논문지B
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• 제33B권9호
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• pp.97-106
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• 1996

In this paper, we propose a face recognition algorithm using the hidden markov model and neural networks (HMM-NN). In the preprocessing stage, we find edges of a face using the locally adaptive threshold (LAT) scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In the training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability vlaues calculated by the HMM to subsequent neural networks (NN) as input data. Computer simulation shows that the proposed HMM-NN algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• 한국정보통신학회논문지
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• 제16권10호
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• pp.2121-2128
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• 2012

본 논문은 Wavelet 변환을 이용한 실시간 얼굴 영역 검출을 제안하였으며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 검출 알고리즘을 제안하였다. 검출된 얼굴 영상은 주성분 분석을 통해 저차원 얼굴 심볼로 구성하여 얼굴을 인식한다. 제안된 방법은 기존의 기하학적인 특징 기반 방법이나 외관기반 방법의 비해 많은 계산 량이 요구 되지 않고 최소한의 정보를 사용하고도 높은 인식률을 유지 할 수 있기에 실시간 시스템 구축에 매우 적합하다. 또한 얼굴 인식 시 발생하는 잘못된 인식이나 인식 오차를 줄이기 위해 고유 공간상에 투영된 모델 특징 값을 군집화 알고리즘을 통해 특정한 기호로 구성하여 은닉마르코프 모델의 입력 기호로 사용하였다. 이렇게 함으로써 임의의 입력 얼굴은 확률 값이 가장 높은 해당 얼굴 모델로 인식하게 된다. 실험 결과 기존의 방식인 Euclidean과 Mahananobis방법 보다 제안한 방법이 잘못된 매칭이나 매칭 실패에서 우수한 인식 성능을 보였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.134-138
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 춘계종합학술대회
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• pp.336-341
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• 2000

Hidden Markov Model(HMM)을 기반으로 한 얼굴 검출과 얼굴 인식에 대한 프레임작업에 대한 것이다. 관찰 벡터는 Karhunen-Loves Transform(KLT)의 상관관계를 이용하여 얻은 HMM의 정역학 특성을 사용하였으며, 본 연구에서 보여준 얼굴인식 방법은 이전의 HMM 기반의 얼굴인식 방법에서 인식률을 약간 개선함으로써 컴퓨터 연산을 훨씬 간단히 할 수 있음을 보여준다

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.148-153
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.