Classification of Facial Expression for Emotion Recognition using Convolutional Neural Network

In this paper, we propose a method to reduce the number of parameters based on a pre-trained deep convolutional neural network (DNN) for a facial expression recognition (FER) task. In order to maintain the high level of accuracy of the pre-trained network and save computational resources through reducing the number of parameters, we first build and train a high accuracy DNN model and extract the layers which contain the most representative facial expression features; then, we connect these layers with lightweight depthwise separable convolutions and global max pooling layers as the re-training network. We train and compare the re-training network with different DNN architectures on two FER datasets (FER2013, AffectNet). The results demonstrate that our method retains its pre-trained high accuracy with fewer model parameters than current state-of-the-art DNN methods.

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, Swin_Tiny_Patch4_Window7_224 achieved the highest classification Accuracy of 99.24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

Facial Expression is a very important factor in the social interaction of human beings. And technologies that can automatically interpret and respond to stimuli of facial expressions already find a wide variety of applications, from antidepressant drug testing to fatigue analysis of drivers and pilots. In this context, the following work presents a model for Automatic Classification of Facial Expression using as a training base the dataset Challenges in Representation Learning (FER2013), characterized by examples of spontaneous facial expressions in uncontrolled environments. The presented method is composed by a Convolutional Neural Networks Ensemble architecture, using a non-trivial voting system, based on a smart model, Xtreme Gradient Boosting - XGBoost. As performance criteria for validation of the proposed model, were used K-fold and F1 Score Micro techniques to guarantee robustness and reliability of the results, which are competitive with state-of-the-art works.

The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pre-trained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction.

A pre-trained deep convolutional neural network (DCNN) is the feed-forward computation perspective which is widely used for the embedded vision systems. In the DCNN, the 2D convolutional operation occupies more than 90% of the computation time. Since the 2D convolutional operation performs massive multiply-accumulation (MAC) operations, conventional realizations could not implement a fully parallel DCNN. The RNS decomposes an integer into a tuple of L integers by residues of moduli set. Since no pair of modulus have a common factor with any other, the conventional RNS decomposes the MAC unit into circuits with different sizes. It means that the RNS could not utilize resources of an FPGA with uniform size. In this paper, we propose the nested RNS (NRNS), which recursively decompose the RNS. It can decompose the MAC unit into circuits with small sizes. In the DCNN using the NRNS, a 48-bit MAC unit is decomposed into 4-bit ones realized by look-up tables of the FPGA. In the system, we also use binary to NRNS converters and NRNS to binary converters. The binary to NRNS converter is realized by on-chip BRAMs, while the NRNS to binary one is realized by DSP blocks and BRAMs. Thus, a balanced usage of FPGA resources leads to a high clock frequency with less hardware. The ImageNet DCNN using the NRNS is implemented on a Xilinx Virtex VC707 evaluation board. As for the performance per area GOPS (Giga operations per second) per a slice, the proposed one is 5.86 times better than the existing best realization.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning-based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.

Images of facial expressions are often captured from various views as a result of either head movements or variable camera position. Existing methods for multiview and/or view-invariant facial expression recognition typically perform classification of the observed expression using either classifiers learned separately for each view or a single classifier learned for all views. However, these approaches ignore the fact that different views of a facial expression are just different manifestations of the same facial expression. By accounting for this redundancy, we can design more effective classifiers for the target task. To this end, we propose a discriminative shared Gaussian process latent variable model (DS-GPLVM) for multiview and view-invariant classification of facial expressions from multiple views. In this model, we first learn a discriminative manifold shared by multiple views of a facial expression. Subsequently, we perform facial expression classification in the expression manifold. Finally, classification of an observed facial expression is carried out either in the view-invariant manner (using only a single view of the expression) or in the multiview manner (using multiple views of the expression). The proposed model can also be used to perform fusion of different facial features in a principled manner. We validate the proposed DS-GPLVM on both posed and spontaneously displayed facial expressions from three publicly available datasets (MultiPIE, labeled face parts in the wild, and static facial expressions in the wild). We show that this model outperforms the state-of-the-art methods for multiview and view-invariant facial expression classification, and several state-of-the-art methods for multiview learning and feature fusion.

Facial expressions are highly dynamic signals that are rarely categorized as static, isolated displays. However, the role of sequential context in facial expression categorization is poorly understood. This study examines the fine temporal structure of expression-based categorization on a trial-to-trial basis as participants categorized a sequence of facial expressions. The results showed that the local sequential context provided by preceding facial expressions could bias the categorical judgments of current facial expressions. Two types of categorization biases were found: (a) Assimilation effects-current expressions were categorized as close to the category of the preceding expressions, and (b) contrast effects-current expressions were categorized as away from the category of the preceding expressions. The effects of such categorization biases were modulated by the relative distance between the preceding and current expressions, as well as by the different experimental contexts, possibly including the factors of face identity and the range effect. Thus, the present study suggests that facial expression categorization is not a static process. Rather, the temporal relation between the preceding and current expressions could inform categorization, revealing a more dynamic and adaptive aspect of facial expression processing.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80–0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52–0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialized modalities. We employ a deep convolutional neural network (CNN) to classify cyst and mass patches. Deep CNNs have been shown to be powerful classifiers, but need a large amount of training data for which medical problems are often difficult to come by. The key contribution of this paper is that we show good performance can be obtained on a small dataset by pretraining the network on a large dataset of a related task. We subsequently investigate the following: (a) when a mammographic exam is performed, two different views of the same breast are recorded. We investigate the merit of combining the output of the classifier from these two views. (b) We evaluate the importance of the resolution of the patches fed to the network. (c) A method dubbed tissue augmentation is subsequently employed, where we extract normal tissue from normal patches and superimpose this onto the actual samples aiming for a classifier invariant to occluding tissue. (d) We combine the representation extracted using the deep CNN with our previously developed features. We show that using the proposed deep learning method, an area under the ROC curve (AUC) value of 0.80 can be obtained on a set of benign solitary cysts and malignant mass findings recalled in screening. We find that it works significantly better than our previously developed approach by comparing the AUC of the ROC using bootstrapping. By combining views, the results can be further improved, though this difference was not found to be significant. We find no significant difference between using a resolution of 100 versus 200 micron. The proposed tissue augmentations give a small improvement in performance, but this improvement was also not found to be significant. The final system obtained an AUC of 0.80 with 95% confidence interval [0.78, 0.83], calculated using bootstrapping. The system works best for lesions larger than 27mm where it obtains an AUC value of 0.87. We have presented a computer-aided diagnosis (CADx) method to discriminate cysts from solid lesion in mammography using features from a deep CNN trained on a large set of mass candidates, obtaining an AUC of 0.80 on a set of diagnostic exams recalled from screening. We believe the system shows great potential and comes close to the performance of recently developed spectral mammography. We think the system can be further improved when more data and computational power becomes available.

First impressions influence the behavior of people towards a newly encountered person or a human-like agent. Apart from the physical characteristics of the encountered face, the emotional expressions displayed on it, as well as ambient information affect these impressions. In this work, we propose an approach to predict the first impressions people will have for a given video depicting a face within a context. We employ pre-trained Deep Convolutional Neural Networks to extract facial expressions, as well as ambient information. After video modeling, visual features that represent facial expression and scene are combined and fed to a Kernel Extreme Learning Machine regressor. The proposed system is evaluated on the ChaLearn Challenge Dataset on First Impression Recognition, where the classification target is the “Big Five” personality trait labels for each video. Our system achieved an accuracy of 90.94 % on the sequestered test set, 0.36 % points below the top system in the competition.

Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p&lt;=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

Abstract: This research paper introduces an innovative approach to music recommendation systems, utilizing facial expression analysis for the delivery of personalized music suggestions. Through the utilization of machine learning algorithms, the system assesses the user's emotional state by analyzing facial expressions, subsequently offering music recommendations aligned with their mood. The proposed methodology incorporates a deep learning-based model for the detection and classification of facial expressions, complemented by a collaborative filtering algorithm to generatepersonalized music recommendations

Due to the growing number of people suffering from stroke or facial nerve damage which influence the quality of life, we propose an acquisition system that identify and classify facial expression by manual analysis of signals using an EMG. Beside these features, the designed system is able to save the records in a database so that these kinds of medical conditions can be seen when they first appeared and how they manifested along the way. The acquisition system has an interface created in MATLAB App Design, which facilitates the experience of signal acquisition and database creation. The signal acquisition occurred during the visual or auditory stimulation of four reactions, by means of videos made for this purpose. The extracted signals were manually segmented and analyzed. For that acquisition system, a cover was designed, and 3D printed, which has both a protective role and an aesthetic role. Although the paper investigates only a few facial expressions, it can be extended in the future to recognize emotions and even to make correlations between different facial expression and other medical conditions recorded in the database.

Facial expressions are non-verbal signs that play an important role between interpersonal communications and also they are the most effective way to describe human emotions. The correct and fast extraction/recognition of the facial expressions is an ongoing research area for computer vision. In this study, the effects of feature selection on the classification of seven different facial expressions (anger, disgust, fear, joy, neutral, sadness, and surprise) are analyzed. To this end, the features for each expression were extracted using Gabor filters from the facial images and selected the best ones using two different feature categories. In the first category, the features that they do exist in one class but do not exist in all other classes have been selected. In the second category, the features that they represent the one class have been selected. These selected features were employed for expression classification using Support Vector Machines (SVMs). Comparative results are given in terms of types for selecting features, feature selection algorithms and the approaches used for the multi-classification. The best results were obtained using RFE algorithm with first type of feature selection and using one-vs-rest approach for training. It was also demonstrated that the feature selection has a positive effect to increase the classification performance comparing when the results were observed with no feature selection.