Browsing by Author "Rouabah, Khaled"

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Local feature selection using the wrapper approach for facial-expression recognition
(Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT), 2024) Boukhobza, Fatima Zohra; Gharbi, Abdenour Hacine; Rouabah, Khaled; Ravier, Phillipe
Automatic Facial Expression Recognition (FER) systems provide an important way to express and interpret the emotional and mental states of human beings. These FER systems transform the facial image into a set of features to train a classifier capable of distinguishing between different classes of emotions. However, the problem often posed is that the extracted feature vectors sometimes contain irrelevant or redundant features, which decreases the accuracy of the induced classifier and increases the computation time. To overcome this problem, dimensionality must be reduced by selecting only the most relevant features. In this paper, we study the impact of adding the "Wrapper" selection approach and using the information provided by different local regions of the face such as the mouth, eyes and eyebrows, on the performance of a traditional FER system based on a local geometric feature-extraction method. The objective here is to test and analyze how this combination can improve the overall performance of the original traditional system. The obtained results, based on the Multimedia Understanding Group (MUG) database, showed that the FER system combined with the proposed feature-selection strategy gives better classification results than the original system for all four classification models; namely, K-Nearest Neighbor (KNN) classifier, Tree classifier, NB classifier and Linear Discriminant Analysis (LDA). Indeed, a considerable reduction (up to 50%) in the number of features used and an accuracy of 100%, using the LDA classifier, were observed, which represents a significant improvement in terms of computation time, efficiency and memory space. Furthermore, the majority of relevant features used are part of the "eyebrows’ region", which proves the importance of using information from local regions of the face in emotion recognition tasks.
A New Facial Expression Recognition Algorithm Based on DWT Feature Extraction and Selection
(Zarka Private University, 2024) Boukhobza, Fatima Zohra; Hacine Gharbi, Abdenour; Rouabah, Khaled
In this paper, we propose an efficient framework to improve accuracy and computational cost of a Facial Expression Recognition (FER) system. This framework is carried out in three stages. In the initial one, corresponding to feature extraction, three descriptors, derived from Discrete Wavelet Transform (DWT), are introduced to extract distinct feature types. In the second stage, focused on feature selection, a Wrapper approach is adopted to carefully select the most relevant features from the previously extracted pool. Following feature selection, the Support Vector Machine (SVM) classifier is employed, in the final stage, to determine an individual's affective state. The experiments were conducted in person-independent mode using both the Japanese Female Facial Expression (JAFFE) and extended Cohn-Kanade (CK+) databases which included the following emotions: anger, disgust, contempt, fear, happy, sad, surprise, and neutral. The obtained results demonstrated the effectiveness of the proposed framework in increasing recognition rate and decreasing response time compared to other state-of-the-art methods. A comparative study between our proposed framework and that based on the Local Binary Patterns (LBP) method demonstrated that our framework outperforms the latter for most emotions. In fact, our proposed framework converges rapidly and achieves good performance, thus allowing us to develop a real-time Facial Expression Recognition (FER) system in person-independent mode. Average recognition rates of 89.66% and 87.76% were obtained using our method with the JAFFE database and the CK+ database, respectively.