• Journal of Information Processing Systems
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• 제17권2호
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• pp.399-410
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• 2021

Aiming at the problem that complex external variables in natural scenes have a greater impact on facial expression recognition results, a facial expression recognition method based on two-stream convolutional neural network is proposed. The model introduces exponentially enhanced shared input weights before each level of convolution input, and uses soft attention mechanism modules on the space-time features of the combination of static and dynamic streams. This enables the network to autonomously find areas that are more relevant to the expression category and pay more attention to these areas. Through these means, the information of irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused by lighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessing chain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIE datasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are better than other comparison methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권12호
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• pp.1260-1270
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• 2005

최근 기계학습 분야에서 커널머신을 이용한 대표적 학습기로 Support Vector Machines (SVM)이 주목 받고 있다. SVM은 통계적 학습이론에 기반하여 뛰어난 일반화 성능을 보여주며, 다양한 패턴인식 문제에 적용되고 있다. 그러나. SVM은 이진 분류기이므로 일반적인 다중 클래스 문제에 곧바로 적용할 수 없다. SVM을 다중 클래스 문제의 하나인 얼굴인식에 도입하기 위한 방법으로는, One-Per-Class와 All-Pairs가 대표적이다. 상기 두 방법은 다중 클래스 문제를 여러 개의 이진 클래스 문제로 분할하고, 이들을 다시 종합하여 최종 결정을 내리는 출력코딩이라는 일반적인 방법에 속한다. 본 논문에서는 이진 분류기인 SVM의 다중 클래스 분류기 확장 방안으로 출력코딩 방법론을 설명한다. 또한 출력코딩 방법론의 대표적인 이론적 기반인 ECOC(Ewor-Correcting Output Codes)를 근간으로 하는 새로운 출력코딩 방법들을 제안하고, 얼굴인식 실험을 통해 SVM을 기반 분류기로 사용할 경우의, 출력코딩 방법의 특성을 비교$\cdot$분석한다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1039-1045
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• 1994

The speech recognition systems using VQ have usually the problem decreasing recognition rate, MSVQ assigning the dissimilar vectors to a segment. In this paper, applying One-stage DMS/DP algorithm to the recognition experiments, we can solve these problems to what degree. Recognition experiment is peformed for Korean DDD area names with DMS model of 20 sections and word unit template. We carried out the experiment in speaker dependent and speaker independent, and get a recognition rates of 97.7% and 81.7% respectively.

• 한국과학교육학회지
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• 제27권4호
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• pp.309-317
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• 2007

이 연구는 우리 과학수업환경에 적합한 문제해결 모형 및 학습자의 문제해결 활동 단계를 안내하는 활동도구를 개발하고, 이를 과학수업에 적용하여 중학생들의 문제해결 활동에 대한 인식을 알아보는데 있다. IDEAL, CPS, SSCS 문제해결 모형을 고찰하여, 문제해결 단계를 문제탐색, 계획실행, 결과발표, 발표평가 등의 4단계 선정하였다. 그리고 문제해결 단계별 활동요소를 선정하여 각각의 문제해결 단계를 안내하는 활동도구를 각각 개발하였다. 중학교 1학년 2개 학급 과학수업에 개발한 문제해결 모형 및 활동도구를 적용하여 중학생들의 문제해결 활동에 대한 인식을 조사하였다. 문제해결 활동은 과학수업에 더 참여적이고, 흥미를 갖게 하며, 실생활의 문제를 해결하는데 도움을 준다고 응답하였다. 따라서 문제해결 활동은 중학생들의 과학수업에 대한 긍정적인 인식을 갖게 하는데 유용한 교수학습활동이며 또한 실생활 소재의 문제해결 활동은 생활 주위에서 일어나는 문제에 흥미와 호기심을 가지고 실생활의 문제를 해결하려는 태도를 기르는데 유용한 교수학습활동이라 사료된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Journal of information and communication convergence engineering
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• 제2권1호
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.19-21
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• 2004

In many cases, human's visual recognition depends on contextual information. We need to use effective feature information for performing vigorous place recognition to illumination, noise, etc. In the existing cases that use edge and color, etc., visual recognition doesn't cope effectively with real environment. To solve this problem, using natural marker, we improve the efficiency of place recognition.

• 로봇학회논문지
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• 제3권3호
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• pp.245-254
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• 2008

Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.