*Result*: An integrated attention-guided deep convolutional neural network for facial expression recognition in the wild.

*Facial expression recognition (FER) in real-world unconstrained conditions is a challenging and active field of research among the pattern recognition and computer vision community. FER systems have immense use in advanced applications based on human-computer interaction (HCI) and human-robot interaction (HRI). Most of these applications heavily rely on the manifestation of hidden emotions of the individuals. However, recognizing facial expressions in complex real-world conditions is difficult for a computer, unlike humans. Over the decades, researchers developed numerous methods for FER in static images. Most of these methods fail to effectively characterize the feature differences among facial expressions and attain desired robustness in real-world scenarios. Besides, these methods are compute-intensive, and their parameters are too large to realize real-time classification of facial expressions on low-cost embedded devices. Hence, to mitigate these challenges and develop a robust and compute-efficient scheme for FER in the wild, this paper proposes a novel integrated attention-guided deep convolutional neural network (IAG-DCNN). The IAG-DCNN model integrates and fine-tunes two lightweight attention-guided DCNNs (AG-DCNNs) initially pre-trained on the FER datasets. The AG-DCNNs use channel attention blocks (CABs) to select relevant convolutional filters, thus alleviating feature map redundancy. Also, the CABs suppress useless information and improve the classification accuracy of the AG-DCNNs. To test the effectiveness of the designed IAG-DCNN model, we performed experiments on the FER2013, RAF-DB, and SFEW datasets. The proposed IAG-DCNN with 2.93M parameters, 2.77 GFLOPs, and 11.50MB of memory storage size attains competitive recognition accuracy on the benchmark FER in the wild datasets. Also, the lightweight IAG-DCNN model with an inference time of 2.96ms significantly improves the overall classification time. [ABSTRACT FROM AUTHOR]

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