Averaged Gabor Filter Features for Facial Expression Recognition

Lajevardi, Seyed Mehdi; Lech, Margaret

doi:10.1109/dicta.2008.12

Cited by 30 publications

(12 citation statements)

References 23 publications

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“…In most cases, a Gabor filter bank with five frequencies and eight orientations is used to extract the features for a face representation [4,10]. The five wavelengths (reciprocals of frequencies) λ m=1,...,5 and the eight orientation angles θ n=1,..., 8 are calculated as:…”

Section: Gabor Wavelet Filtersmentioning

confidence: 99%

Automatic facial expression recognition: feature extraction and selection

Lajevardi

Hussain

2010

SIViP

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In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.

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Section: Gabor Wavelet Filtersmentioning

confidence: 99%

Automatic facial expression recognition: feature extraction and selection

Lajevardi

Hussain

2010

SIViP

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“…A number of research works on expression recognition using Gabor features have reported improved recognition rate [16]- [18]. In our work, Gabor features are used as the texture descriptor.…”

Section: B Texture Featurementioning

confidence: 99%

A scalable metric learning-based voting method for expression recognition

Wan¹,

Aggarwal²

2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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In this paper, we propose a facial expression classification method using metric learning-based k-nearest neighbor voting. To achieve accurate classification of a facial expression from frontal face image, we first learn a distance metric structure from training data that characterizes the feature space pattern, then use this metric to retrieve nearest neighbors from training dataset, and finally output the classification decision accordingly. An expression is represented as a fusion of face shape and texture. This representation is based on registering a face image with landmarking shape model and extracting Gabor features from local patches around landmarks. This type of representation achieves robustness and effectiveness by using an ensemble of local patch feature detector at a global shape level. A naive implementation of metric learning-based k-nearest neighbor would incur a time complexity proportional to the size of the training dataset, which precludes this method being used with enormous dataset. To scale to potential larger databases, an approximate yet efficient variant scheme of MLbased kNN voting is further devised based on Locality Sensitive Hashing (LSH). A query example is directly hashed to the bucket of a pre-computed hash table where candidate nearest neighbors can be found and there is no need to search the entire database for nearest neighbors. Experimental results on Cohn-Kanade database and Moving Faces and People database show that both ML-based kNN voting and its LSH approximation outperform the state-of-the-art, demonstrating the superiority and scalability of our method.

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“…Features are extracted from the filters. A Gabor filter faced facial features obtained by employing filter banks with five frequencies and eight orientations is employed in [11], [12]. A K-Nearest Neighbors classifier principal component analysis based technique is used in the study.…”

Section: Introductionmentioning

confidence: 99%

Human emotion recognition: An interval type-2 fuzzy inference system based approach

Subramanian

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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In this paper, an Interval Type-2 neuro-fuzzy inference system based emotion recognition system is proposed. The employed fuzzy inference system is a four layer network realizing Takagi-Sugeno-Kang fuzzy inference mechanism, with an input layer, a rule layer, a normalization layer and an output layer. The rule layer employs an Interval Type-2 fuzzy membership function to handle the uncertainty in the facial emotions of different individuals. The rules for this network is generated by employing a meta-cognitive projection based learning algorithm. The aim of the proposed approach is to approximate the decision surface separating different emotions based on noisy input features. During learning, as a sample is presented to the network, it calculates the prediction error and knowledge content in the sample to decide on whether to learn the sample, when to learn it and which technique to use to learn it. The meta-cognitive learning mechanism helps in avoiding overtraining and achieving better generalization performance. The projection based learning approach employed to learn the knowledge in a given sample works by minimizing the total energy in the network in a linear least squared sense.The proposed emotion recognition system is tested on two wellknown publicly available datasets: Japanese female facial expression dataset and Taiwanese female expression image database. Local binary pattern based features are extracted from these databases, as they have been shown to describe facial features such as edges and spots, efficiently. Two different studies are performed: a 5-fold cross-validation study on the emotion recognition ability of the system and a database independent study. The performance comparison with other approaches clearly highlights the advantage of the proposed system.

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Averaged Gabor Filter Features for Facial Expression Recognition

Cited by 30 publications

References 23 publications

Automatic facial expression recognition: feature extraction and selection

Automatic facial expression recognition: feature extraction and selection

A scalable metric learning-based voting method for expression recognition

Human emotion recognition: An interval type-2 fuzzy inference system based approach

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