## 1 Introduction  
In recent years, because of the rapid development of graphics processing unit (GPU) technology, hardware and computation performance in the research and application of artificial intelligence have improved considerably. Accordingly, machine learning technologies based on neural networks (NNs) have also seen rapid advancement. Common NNs include the recurrent NN, which is suitable for handling tasks in a specific context, and convolutional NN (CNN), which is suitable for image processing. Evidence has shown that NNs with deep-learning technology excel at classification tasks. Moreover, such NNs can achieve exceptional results in research and application in various fields through design of a suitable NN framework based on the task features, so long as the task in question can be transformed into a classification task. In particular, biometric identification is the primary field exhibiting marked development in the use of NN technology. In contrast to conventional handcrafted features, which are intuitive and observable by human eyes, higher level abstract features extracted using deep learning NNs contain extremely detailed information that is difficult for humans to understand, particularly the features acquired from deep NN. Regarding biometrics, gait recognition refers to the analysis of pedestrians' walking habits and postures to obtain a variety of information. Depending on the features of the task at hand, gait data can be obtained by observing and recording skeletal movements with instruments, such as wearable devices and multiple cameras, and through clinical measurement or analyses of image sequences to identify a specific pedestrian. Moreover, in contrast to other biometric identification such as iris recognition and fingerprint recognition--both of which require collecting biometric data from users--gait-based pedestrian recognition does not require collaboration from pedestrians in biometric feature collection because cameras are set up at a distance. Moreover, habitual movements are difficult to alter over a short period. Therefore, gait-based pedestrian recognition is not subject to changes in appearance (e.g., face coverage or outfit change) and is considerably more applicable to security systems--such as surveillance cameras--than is facial recognition, which requires complete facial images. Numerous scholars have conducted research on this topic in recent years. Gait recognition is similar to activity recognition, which involves series of analyses of human action sequences. Therefore, dense optical flow, which is often used in activity recognition, is considered effective in gait recognition. Dense optical flow eliminates back-ground influences and effectively captures low-level features of pedestrian activities. A deep-learning NN was applied in this study to analyze pedestrian gait, and high-level abstract features of walking manners were extracted to conduct biometric recognition tasks. This study incorporated deep-learning NNs to conduct gait recognition. Such NNs include the pretrained NN, which extracts dense optical flow. This process obtained the moving speed of pixels between two consecutive images in a color pedestrian image sequence and eliminated influence from appearance features and backgrounds. Subsequently, a pedestrian detection NN was applied to obtain pedestrian location by focusing on the region of the pedestrian in question. By processing a sizable gait database, this study obtained a sufficient number of samples to train a deep learning NN that can effectively extract pedestrian gait features. This was followed by verification of the performance and reliability of the deep-learning NN. Research on gait analysis and recognition methods has involved various aspects such as portrait segmentation and skeleton tracking, which are similar to activity recognition [1; 2]. Favorable outcomes have been achieved on activity recognition by inputting the RGB color model and optical flow into the NN.