# Deep Learning for Intelligent Human-Computer Interaction
## 5 Human Gesture Recognition Based on Deep Learning
Then, the training is performed by auxiliary classification loss with a global average pooling layer to focus on discriminant non-moving regions in video frames. The STA-CNN model delivers state-of-the-art performance on two of the most challenging datasets, namely, UCF-101 and HMDB-51. Human behavior recognition has become the focus of the wider adoption of computer vision. Recognizing the ambiguity of movement comes not only from the difficulty of defining body part movements but also from real-world problems, such as camera movements, dynamic backgrounds, and harsh weather. To assess the performance of these methods quantitatively and qualitatively, common datasets representing various operations under multiple conditions and constraints are recorded. [PERSON] et al. (2020) [77] summarized it according to the types of problems solved by existing methods. In addition, existing datasets introduced for the field of human behavior recognition were compared. Human motion recognition in video is a difficult task because of its complex background, geometric transformation, and massive data. Computer vision-based video action recognition is widely used in video surveillance, behavior detection, HCI, medical-aided diagnosis, and motion analysis. However, video action recognition may be affected by many factors, such as background and illumination. Dual-stream convolutional neural networks are trained with video spatiotemporal models and fused at the output. The multisegment dual-stream convolutional neural network model trains spatiotemporal information from video, extracts its features and fuses them, and then determines the category of video action. [PERSON] et al. (2021) [78] used the Google Xception model and transfer learning in their study and took the Xception model trained on ImageNet as the initial weight, which solved model underfitting caused by insufficient video behavior datasets and can effectively reduce the influence of various factors in the video. This approach also greatly improves accuracy and reduces training time. More importantly, the kinetics400 dataset was used for pretraining to compensate for the lack of datasets, which greatly improved the accuracy of the model. The human action recognition process in the video is shown in Figure 6.  
To achieve human action recognition in videos, [PERSON] (2020) [79] developed an effective algorithm that can use a single decisive posture to identify human behavior in videos. To accomplish the task, the optical flow was utilized to extract the deterministic posture and the feature was extracted by the double transform of wavelet. The double transform was accomplished by the Gabor wavelet transform and Ridgelet transform. Gabor wavelet transforms generate eigenvectors by computing first-order statistics of various proportions and directions of input poses, which are robust to translation, scaling, and rotation. The Ridgelet transform was utilized to calculate the direction-dependent shape features of human behavior. The fusion features provide a powerful algorithm. The validity of the algorithm was measured on the public Weizmann, Ballet, Movement, and UT Interaction datasets, which reported accuracies of 96.66%, 96%, 92.75%, and 100%, respectively. Comparison of accuracy with related advanced techniques showed the excellent performance of the research. Similarly, [PERSON] et al. (2020) [80] proposed a new method for real-time fingertip detection and gesture recognition using a depth camera and a 3D convolutional neural network. The system can accurately and reliably extract the position of fingertips and recognize gestures in real-time. They demonstrated the accuracy and robustness of the interface by evaluating the gesture recognition of various gestures. In addition, a tool was developed to manipulate computer programs to show the possibility of using gesture recognition. Experimental results showed that the proposed system has high gesture recognition accuracy. Therefore, it is deemed a good method for the future of manual HCI-based gesture interfaces.