VALID: A New Practical Audio-Visual Database, and Comparative Results

Fox, Niall; O'Mullane, Brian; Reilly, Richard B.

doi:10.1007/11527923_81

Cited by 33 publications

(15 citation statements)

References 14 publications

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“…We have gathered 252 images from the VALID Database [25], 24 from the CALTECH's Faces 1999 Database † , and 23 images captured by ourselves, for a total of 300 face images. The former databases are used for facial expression recognition; therefore, we have used only images where users had their mouth closed.…”

Section: Results For Mouth Corners Detectionmentioning

confidence: 99%

Detection of Tongue Protrusion Gestures from Video

Sapaico

Laga

Nakajima

2011

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a system that, using video information, segments the mouth region from a face image and then detects the protrusion of the tongue from inside the oral cavity. Initially, under the assumption that the mouth is closed, we detect both mouth corners. We use a set of specifically oriented Gabor filters for enhancing horizontal features corresponding to the shadow existing between the upper and lower lips. After applying the Hough line detector, the extremes of the line that was found are regarded as the mouth corners. Detection rate for mouth corner localization is 85.33%. These points are then input to a mouth appearance model which fits a mouth contour to the image. By segmenting its bounding box we obtain a mouth template. Next, considering the symmetric nature of the mouth, we divide the template into right and left halves. Thus, our system makes use of three templates. We track the mouth in the following frames using normalized correlation for mouth template matching. Changes happening in the mouth region are directly described by the correlation value, i.e., the appearance of the tongue in the surface of the mouth will cause a decrease in the correlation coefficient through time. These coefficients are used for detecting the tongue protrusion. The right and left tongue protrusion positions will be detected by analyzing similarity changes between the right and left half-mouth templates and the currently tracked ones. Detection rates under the default parameters of our system are 90.20% for the tongue protrusion regardless of the position, and 84.78% for the right and left tongue protrusion positions. Our results demonstrate the feasibility of real-time tongue protrusion detection in vision-based systems and motivates further investigating the usage of this new modality in humancomputer communication. key words: face gestures, mouth segmentation, perceptual user interface, tongue protrusion, vision-based systems

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Section: Results For Mouth Corners Detectionmentioning

confidence: 99%

Detection of Tongue Protrusion Gestures from Video

Sapaico

Laga

Nakajima

2011

IEICE Trans. Inf. & Syst.

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“…• The VALID database [48], which consists of five recording sessions of 106 subjects over a period of one month. One session is recorded in a studio with controlled lighting and no background noise, and the other four sessions are recorded in office-type scenarios; • The color FERET database [49], which is composed of 11,338 face images of 994 different persons, taken from different angles of view, over the course of 15 sessions between 1993 and 1996.…”

Section: Results For the Proposed Fuzzy Systemsmentioning

confidence: 99%

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Pujol

Jimeno-Morenilla

López

2017

Entropy

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Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

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“…Performance has been analyzed according to various dimensions with VALID Audio-Visual database [34] to measure the performance of the proposed appearance and shape based face recognition system. Though VALID is a multimodal Audio-Visual database, it provides 106 subjects each with four office lighting conditions, gathered periodically over one month giving some temporal variation and one studio session with controlled lighting.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations

Islam¹

2013

CSSP

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This paper presents an approach of face recognition system using Backpropagation learning neural network algorithm introducing appearance and shape based facial features to enhance the efficiency with different lighting variations. To extract the appearance and shape based facial feature, Active Appearance Model (AMM) has been applied. Appearance based facial feature is useful when the lighting condition is uniform. On the other hand when the environmental lighting condition is different, shape based facial features can perform better in comparison with appearance based feature because shape based structure is not changed with lighting variations. In this work, both appearance and shape based facial features are combined to enhance the recognition efficiency for various light variant system. For dimensionality reduction of appearance and shape based facial features, Principal Component Analysis (PCA) method has been used. Finally, error Backpropagation learning feed forward neural network algorithm has been used to classify the facial features. To measure the performance of the proposed appearance and shape based facial recognition system, VALID database has been used where each face has been captured with four different lighting variations. Experiments have been performed with Appearance-Only, Shape-Only and combined Appearance-Shape based feature and performance of the proposed system shows the superiority of the face recognition system.

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VALID: A New Practical Audio-Visual Database, and Comparative Results

Cited by 33 publications

References 14 publications

Detection of Tongue Protrusion Gestures from Video

Detection of Tongue Protrusion Gestures from Video

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations

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