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Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state‐of‐the‐art accuracy in the facial analysis task. However, recognizing facial expressions in the wild with high confidence running on a low‐cost embedded device remains challenging. To this end, this study presents an efficient dual‐channel ensembled deep CNN (DCE‐DCNN) for FER in the wild. Initially, two DCNNs, namely the DCNNG$$ {\mathrm{DCNN}}_G $$ and DCNNS$$ {\mathrm{DCNN}}_S $$, are trained separately on the grayscale and Scharr‐convolved vertical gradient facial images, respectively. The proposed network later integrates the two pre‐trained DCNNs to obtain the dual‐channel integrated DCNN (DCI‐DCNN). Finally, all three neural networks, namely the DCNNG$$ {\mathrm{DCNN}}_G $$, DCNNS$$ {\mathrm{DCNN}}_S $$, and DCI‐DCNN, are jointly fine‐tuned to get a single dual‐channel‐multi‐output model. The multi‐output model produces three prediction scores for the given input facial image. The prediction scores are thus fused using the max‐voting ensemble scheme to obtain the DCE‐DCNN with the final classification label. On the FER2013, RAF‐DB, NCAER‐S, AffectNet, and CKPlus benchmark FER datasets, the proposed DCE‐DCNN consistently outperforms the two individual DCNNs and numerous state‐of‐the‐art CNNs. Moreover, the network achieves competitive recognition accuracy on all four FER in the wild datasets with reduced memory storage size and parameters. The proposed DCE‐DCNN model with high throughput on resource‐limited embedded devices is suitable for applications that seek real‐time classification of facial expressions in the wild with high confidence. [ABSTRACT FROM AUTHOR]

Early diagnosis of brain tumor using magnetic resonance imaging (MRI) is vital for timely medication and effective treatment. But, most people living in remote areas do not have access to medical experts and diagnosis facilities. Nevertheless, recent advancement in the Internet of Thing and artificial intelligence is transforming the healthcare system and has led to the development of the Internet of Medical Things (IoMT). An automated brain tumor classification system integrated with the IoMT framework can aid in remotely diagnosing brain tumors. However, the existing methods for brain tumor classification in MRI based on traditional machine learning and deep learning are compute-intensive. Deployment of these methods in the real-world clinical setup poses a serious challenge. Therefore, there is a requirement for robust and compute-efficient techniques for brain tumor classification. To this end, this paper presents a novel lightweight attention-guided convolutional neural network (AG-CNN) for brain tumor classification in magnetic resonance (MR) images. The designed architecture uses channel-attention blocks to focus on relevant regions of the image for tumor classification. Besides, AG-CNN uses skip connections via global-average pooling to fuse features from different stages. This approach helps the network extract enhanced features essential to differentiate tumor and normal brain MR images. To access the efficacy of the designed neural network, we evaluated it on four benchmark brain tumor MRI datasets. The comparison results with the existing state-of-the-art methods revealed the robustness and computational efficiency of the proposed AG-CNN model. The designed brain tumor classification pipeline can be easily deployed on a resource-constrained embedded platform and used in real-world clinical settings to quickly classify brain tumors in MR images. [ABSTRACT FROM AUTHOR]

In the past decade, facial emotion recognition (FER) research saw tremendous progress, which led to the development of novel convolutional neural network (CNN) architectures for automatic recognition of facial emotions in static images. These networks, though, have achieved good recognition accuracy, they incur high computational costs and memory utilization. These issues restrict their deployment in real-world applications, which demands the FER systems to run on resource-constrained embedded devices in real-time. Thus, to alleviate these issues and to develop a robust and efficient method for automatic recognition of facial emotions in the wild with real-time performance, this paper presents a novel deep integrated CNN model, named EmNet (Emotion Network). The EmNet model consists of two structurally similar DCNN models and their integrated variant, jointly-optimized using a joint-optimization technique. For a given facial image, the EmNet gives three predictions, which are fused using two fusion schemes, namely average fusion and weighted maximum fusion, to obtain the final decision. To test the efficiency of the proposed FER pipeline on a resource-constrained embedded platform, we optimized the EmNet model and the face detector using TensorRT SDK and deploy the complete FER pipeline on the Nvidia Xavier device. Our proposed EmNet model with 4.80M parameters and 19.3MB model size attains notable improvement over the current state-of-the-art in terms of accuracy with multi-fold improvement in computational efficiency. [ABSTRACT FROM AUTHOR]

Automatic recognition of facial expressions in the wild is a challenging problem and has drawn a lot of attention from the computer vision and pattern recognition community. Since their emergence, the deep learning techniques have proved their efficacy in facial expression recognition (FER) tasks. However, these techniques are parameter intensive, and thus, could not be deployed on resource-constrained embedded platforms for real-world applications. To mitigate these limitations of the deep learning inspired FER systems, in this paper, we present an efficient dual integrated convolution neural network (DICNN) model for the recognition of facial expressions in the wild in real-time, running on an embedded platform. The designed DICNN model with just 1.08M parameters and 5.40 MB memory storage size achieves optimal performance by maintaining a proper balance between recognition accuracy and computational efficiency. We evaluated the DICNN model on four FER benchmark datasets (FER2013, FERPlus, RAF-DB, and CKPlus) using different performance evaluation metrics, namely the recognition accuracy, precision, recall, and F1-score. Finally, to provide a portable solution with high throughput inference, we optimized the designed DICNN model using TensorRT SDK and deployed it on an Nvidia Xavier embedded platform. Comparative analysis results with the other state-of-the-art methods revealed the effectiveness of the designed FER system, which achieved competitive accuracy with multi-fold improvement in the execution speed. [ABSTRACT FROM AUTHOR]

Existing techniques for Yoga pose recognition build classifiers based on sophisticated handcrafted features computed from the raw inputs captured in a controlled environment. These techniques often fail in complex real-world situations and thus, pose limitations on the practical applicability of existing Yoga pose recognition systems. This paper presents an alternative computationally efficient approach for Yoga pose recognition in complex real-world environments using deep learning. To this end, a Yoga pose dataset was created with the participation of 27 individual (8 males and 19 females), which consists of ten Yoga poses, namely Malasana, Ananda Balasana, Janu Sirsasana, Anjaneyasana, Tadasana, Kumbhakasana, Hasta Uttanasana, Paschimottanasana, Uttanasana, and Dandasana. To capture the videos, we used smartphone cameras having 4 K resolution and 30 fps frame rate. For the recognition of Yoga poses in real time, a three-dimensional convolutional neural network (3D CNN) architecture is designed and implemented. The designed architecture is a modified version of the C3D architecture initially introduced for the recognition of human actions. In the proposed modified C3D architecture, the computationally intensive fully connected layers are pruned, and supplementary layers such as the batch normalization and average pooling were introduced for computational efficiency. To the best of our knowledge, this is among the first studies, which utilized the inherent spatial–temporal relationship among Yoga poses for their recognition. The designed 3D CNN architecture achieved test recognition accuracy of 91.15% on the in-house prepared Yoga pose dataset consisting of ten Yoga poses. Furthermore, on the publicly available dataset, the designed architecture achieved competitive test recognition accuracy of 99.39%, along with multifold improvement in the execution speed compared to the existing state-of-the-art technique. To promote further study, we will make the in-house created Yoga pose dataset publicly available to the research community. [ABSTRACT FROM AUTHOR]