Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Facial Emotion Recognition Based on Empirical Mode Decomposition and Discrete Wavelet Transform Analysis

This paper presents a new framework of using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) with an application for facial emotion recognition. EMD is a multi-resolution technique used to decompose any complicated signal into a small set of intrinsic mode functions (IMFs) based on sifting process. In this framework, the EMD was applied on facial images to extract the informative features by decomposing the image into a set of IMFs and residue. The selected IMFs was then subjected to DWT in which it decomposes the instantaneous frequency of the IMFs into four sub band. The approximate coefficients (cA1) at first level decomposition are extracted and used as significant features to recognize the facial emotion. Since there are a large number of coefficients, hence the principal component analysis (PCA) is applied to the extracted features. The k-nearest neighbor classifier is adopted as a classifier to classify seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise). To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Based on the results obtained, the proposed method demonstrates the recognition rate of 80.28%, thus it is converging

Klasifikasi Menggunakan Metode Hybrid Bayessian-Neural Network (Studi Kasus: Identifikasi Virus Komputer)

Virus komputer merupakan suatu program yang menginfeksi komputer terutama pada saat komputer sedang beroperasi dan menjadi momok bagi pengguna komputer. Virus komputer dapat menggandakan dirinya sendiri dan menyebar dengan cara menyisipkan dirinya pada program dan data lainnya. Efek negatif virus komputer adalah memperbanyak dirinya sendiri, yang membuat sumber daya pada komputer terutama penggunaan memori menjadi berkurang secara signifikan. Diperlukan suatu penangkal atau antivirus dalam mencegah penyebaran yang lebih jauh dalam sistem komputer. Pada penelitian ini, dilakukan suatu identifikasi virus dengan menggabungkan dua metode yaitu Naïve Bayes Classifier dengan Neural Network. Fitur virus didapatkan dari mengkodekan ciri-ciri dari virus. Untuk klasifikasi awal digunakan metode Naïve Bayes Classifier untuk membagi dua jenis fitur, yaitu virus dan bukan virus. Setelah masuk kedalam jenis virus, maka diklasifikasikan kedalam dua jenis virus yaitu trojan atau worm menggunakan salah satu metode neural network (perceptron). Hasil sistem setelah dilakukan uji coba didapatkan recognition rate tertinggi yaitu sebesar 94.12%

Klasifikasi Menggunakan Metode Hybrid Bayessian-Neural Network (Studi Kasus: Identifikasi Virus Komputer)

Virus komputer merupakan suatu program yang menginfeksi komputer terutama pada saat komputer sedang beroperasi dan menjadi momok bagi pengguna komputer. Virus komputer dapat menggandakan dirinya sendiri dan menyebar dengan cara menyisipkan dirinya pada program dan data lainnya. Efek negatif virus komputer adalah memperbanyak dirinya sendiri, yang membuat sumber daya pada komputer terutama penggunaan memori menjadi berkurang secara signifikan. Diperlukan suatu penangkal atau antivirus dalam mencegah penyebaran yang lebih jauh dalam sistem komputer. Pada penelitian ini, dilakukan suatu identifikasi virus dengan menggabungkan dua metode yaitu Naïve Bayes Classifier dengan Neural Network. Fitur virus didapatkan dari mengkodekan ciri-ciri dari virus. Untuk klasifikasi awal digunakan metode Naïve Bayes Classifier untuk membagi dua jenis fitur, yaitu virus dan bukan virus. Setelah masuk kedalam jenis virus, maka diklasifikasikan kedalam dua jenis virus yaitu trojan atau worm menggunakan salah satu metode neural network (perceptron). Hasil sistem setelah dilakukan uji coba didapatkan recognition rate tertinggi yaitu sebesar 94.12%

Facial Expression Recognition Based on Radon and Discrete Wavelet Transform using Support Vector Machines

Extracting facial features remains a difficult task because of unpredictable of facial features largely due to variations in pixel intensities and subtle changes of facial features. The Radon transform inherits rotational and translational properties that are capable of preserving pixel intensities variations and also is used to derive the directional features. Thus, this paper presents a new pattern framework for facial expression recognition based on Radon and wavelet transform using Support Vector Machines classifier to recognize the seven facial emotions. Firstly, the pre-processed facial images are projected into Radon space via Radon transform at a specified angle. Then, the obtained Radon space or sinogram that represent the facial emotions is subjected to wavelet transform. In this framework, the Radon space is decomposed into four sub-band at a different level of decomposition. The approximate coefficients sub-band are independently extracted and used as intrinsic features to recognize the facial emotion. To reduce the data dimensionality, principal component analysis (PCA) is applied to the extracted features. Then, the Support Vector Machines (SVM) classifier is adopted as a classifier to classify seven (anger, disgust, fear, happiness, neutral, sadness and surprise) facial emotions. To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Experimental results show that the proposed method has achieved 93.89% accuracy

Intelligent pixels of interest selection with application to facial expression recognition using multilayer perceptron

International audienceThis paper presents an automatic way to discover pixels in a face image that improves the facial expression recognition results. Main contribution of our study is to provide a practical method to improve classification performance of classifiers by selecting best pixels of interest. Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP. Then, it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem. It eliminates irrelevant data and improves the classification performance using backward feature elimination. Experimental studies on GENKI, JAFFE and FERET databases showed that the proposed system improves the classification results by selecting the best pixels of interest

Intelligent pixels of interest selection with application to facial expression recognition using multilayer perceptron

International audienceThis paper presents an automatic way to discover pixels in a face image that improves the facial expression recognition results. Main contribution of our study is to provide a practical method to improve classification performance of classifiers by selecting best pixels of interest. Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP. Then, it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem. It eliminates irrelevant data and improves the classification performance using backward feature elimination. Experimental studies on GENKI, JAFFE and FERET databases showed that the proposed system improves the classification results by selecting the best pixels of interest