A Coarse-to-Fine Classification Scheme for Facial Expression Recognition

Feng, Xiaoyi; Hadid, Abdenour; Pietikäinen, Matti

doi:10.1007/978-3-540-30126-4_81

Cited by 48 publications

(32 citation statements)

References 11 publications

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“…It is also observed that some local facial regions contain more discriminative information for facial expression classification than others [2], [3], [4]. These studies show that it is reasonable to assign higher weights for the most important facial regions to improve facial expression recognition performance.…”

Section: Introductionmentioning

confidence: 94%

“…In many cases, weights are designed empirically, based on the observation [2], [3], [4]. Here, the Fisher separation criterion is used to learn suitable weights from the training data [11].…”

Section: Block Weightsmentioning

confidence: 99%

“…This approach has been used successfully also for facial expression recognition [2], [3], [4]. LBP features from each block are extracted only from static images, meaning that temporal information is not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

Taini

Zhao

2009

Image Analysis

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Abstract. This paper presents a novel weight-based approach to recognize facial expressions from the near-infrared (NIR) video sequences. Facial expressions can be thought of as specific dynamic textures where local appearance and motion information need to be considered. The face image is divided into several regions from which local binary patterns from three orthogonal planes (LBP-TOP) features are extracted to be used as a facial feature descriptor. The use of LBP-TOP features enables us to set different weights for each of the three planes (appearance, horizontal motion and vertical motion) inside the block volume. The performance of the proposed method is tested in the novel NIR facial expression database. Assigning different weights to the planes according to their contribution improves the performance. NIR images are shown to deal with illumination variations comparing with visible light images.

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Section: Introductionmentioning

confidence: 94%

Section: Block Weightsmentioning

confidence: 99%

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Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

Taini

Zhao

2009

Image Analysis

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“…Recently, LBP is considered as an effective texture classification methodology which was proposed for describing the local structure of an image. LBP and its variants can be uniform and/or rotation invariant [21] and have been extensively exploited in many applications, for instance, facial image analysis, including face detection [22][23][24][25], face recognition and facial expression analysis [26][27][28][29][30][31][32][33][34]; demographic (gender, race, age, etc.) classification [35][36][37][38]; moving object detection [39], etc.…”

Section: Texture Based Classificationmentioning

confidence: 99%

An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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“…Image texture analysis is an important fundamental problem in computer vision. During the past few years, several authors have developed theoretically and computationally simple, but very efficient nonparametric methodology for texture analysis based on LBP [2,3,4,5,6,7,8,9]. The LBP texture analysis operator is defined as a grayscale invariant texture measure, derived from a general definition of texture in a local neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Texture Information from Fuzzy Run Length Matrix

Venkateswarlu¹,

Sujatha²,

Kumar³

2012

IJCA

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For a precise texture classification and analysis, a run length matrix is constructed on the Local Binary pattern using fuzzy principles in the present paper. The proposed Run Length Matrix on Fuzzy LBP (RLM-FLBP) overcomes the disadvantages of the previous run length methods of texture classification that exist in the literature. LBP is a widely used tool for texture classification based on local features. The LBP does not provide greater amount of discriminate information of the local structure and it has a various other disadvantages. The main disadvantage of LBP is, that it compares the centre pixel value with its neighbors to derive the one of the three possible values {0, 1, 2}. The basic drawback of this comparison is that it is very sensitive to noise. And a major contrast between the central pixel and its surroundings are easily resulted by the slight fluctuations above or below the value of the Centre Pixel (CP) and its surroundings. To overcome this problem and to represent the missing local information effectively in the LBP, the present study introduced the concept of fuzzy logic on LBP. This overcomes the problem related to noise and contrast. The proposed method initially converts the 3×3 neighborhood in to fuzzy LBP. In the second stage the proposed method constructs the Run Length Matrix on Fuzzy LBP (RLM-FLBP). On these RLM-FLBP texture features are evaluated for a precise texture classification.

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A Coarse-to-Fine Classification Scheme for Facial Expression Recognition

Cited by 48 publications

References 11 publications

Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

An Automated System for Garment Texture Design Class Identification

Extraction of Texture Information from Fuzzy Run Length Matrix

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