Facial interaction between animated 3D face robot and human beings

Kobayashi, Hiroshi; Hara, Fumio

doi:10.1109/icsmc.1997.633250

Cited by 79 publications

(58 citation statements)

References 10 publications

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“…The minimum distance classifier is used for recognition of six basic emotions. Recognition result is 84.5% Kobayasi and Hara [9] used 234x50x6 neural back propagation network for recognition of 6 basic emotions.…”

Section:  Expression Recognitionmentioning

confidence: 99%

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Recognition of Human Facial Expression through Component Analysis

udadhe¹

2015

IJIRCCE

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ABSTRACT:Face recognition system is a complex image-processing problem in real world applications with complex effects of illumination, occlusion, and imaging condition on the live images. It is a combination of face detection and recognition techniques in image analyzes. Detection application is used to find position of the faces in a given image. Recognition algorithm is used to classify given images with known structured properties, which are used commonly in most of the computer vision applications. On a basis of the extensive study of different approaches to the problem of face action representation, appropriate algorithms are selected for each stage of a system.

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Section:  Expression Recognitionmentioning

confidence: 99%

“…Vertical and horizontal histogram analysis is used to find face boundaries. Then, face contour is obtained by thresholding the image with HSV color space values.Kobayashi and Hara [9] used image captured in monochrome mode to find face brightness distribution. Position of face is estimated by iris localization.…”

Section:  Face Detectionmentioning

confidence: 99%

Recognition of Human Facial Expression through Component Analysis

udadhe¹

2015

IJIRCCE

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“…Kobayashi and Hara (Kobayashi & Hara, 1997) proposed a geometric face model based on 30 facial characteristic points for the frontal face view as shown in Figure 4(a). Subsequently, the point-based model was extended to include 10 extra facial characteristic points on the side view of the face (Pantic & Rothkranthz, 2000) as shown in Figure 4(b).…”

Section: Facial Expression Representationsmentioning

confidence: 99%

Facial Expression Recognition

Matuszewski¹,

Quan²,

Shark³

2011

Biometrics - Unique and Diverse Applications in Nature, Science, and Technology

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“…A classification accuracy of 86% was achieved in this study. Hara and Kobayashi [8,9] also used neural networks approach. The training dataset consisted of from data of 15 subjects (90 images) and these networks were tested using data from another 15 subjects.…”

Section: Introductionmentioning

confidence: 99%

“…The FER system can be applied in different areas of life such security and surveillance, they can predict the offender or criminal"s behavior by analyzing the images of their faces that are captured by the control-camcorder. Furthermore, the FER system has been used in communication to make the "answer machine" more interactive with people.FER has attracted Padgett [7], Hara and Kobayashi [8,9], Zhang [10] and Zhao [11] used neural network approach for expression classification. They classified images into six or seven emotional categories.…”

Section: Introductionmentioning

confidence: 99%

Facial Image based Emotion Recognition System using Neural Network

Dudul¹,

Tayade²,

Talele³

2017

International Journal of Advanced Research in Computer and Comm

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Facial emotion plays a vital role for human interactive communication and also used in numerous real applications. Facial expression identification from frontal still images has in recent times become a hopeful investigation area. Their applications include human-computer interface, human emotion examination robot control, driver state surveillance and medical fields. This paper aims to develop emotion classification scheme to identify seven dissimilar facial emotions, such as surprise, sad, neutral, happy, fear, disgust and anger by using JAFFE database. Two different approaches of feature selection and extraction have been used for generation of optimal feature vector. LBP and 2D-DCT coefficients are employed in addition to image statistics, texture and entropy parameters. In order to reduce the high dimensionality of the inputs, the principal component analysis has been used and significant reduction in the input-dimensionality has been achieved. The single hidden layer feed forward neural network has been used as a classifier in order to classify different emotions from frontal facial images. Three learning algorithms such as resilient backpropagation, scaled conjugate gradient and gradient descent algorithm with momentum and adaptive learning rate have been compared. It has been observed that our meticulously designed LBP based hybrid feature vector and a single hidden layer neural network containing only 70 neurons in the hidden layer trained with gradient descent algorithm with momentum and adaptive learning rate delivers the maximum average overall classification accuracy of 97.2%, which has not been reported so far in the literature for the said database. The proposed neural network is very compact, as it is comprised of only 5,607 connection weights including biases.

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Facial interaction between animated 3D face robot and human beings

Cited by 79 publications

References 10 publications

Recognition of Human Facial Expression through Component Analysis

Recognition of Human Facial Expression through Component Analysis

Facial Expression Recognition

Facial Image based Emotion Recognition System using Neural Network

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