At present, the research of face frontalization mainly solves the face yaw problem, and pays less attention to the face frontalization of the side face affected by yaw and pitch at the same time in real scenes such as surveillance video. Aiming at this problem and the problem of incomplete identity information retained in front face image generated by multi-angle side faces, a Generative Adversarial Network (GAN) based on feature map symmetry and periocular feature preserving loss was proposed. Firstly, according to the prior of face symmetry, a symmetry module of the feature map was proposed. The face key point detector was used to detect the position of nasal tip point, and mirror symmetry was performed to the feature map extracted by the encoder according to the nasal tip, so as to alleviate the lack of facial information at the feature level. Finally, benefiting from the idea of periocular recognition, the periocular feature preserving loss was added in the existing identity preserving method of generated image to train the generator to generate realistic and identity-preserving front face image. Experimental results show that the facial details of the images generated by the proposed algorithm were well preserved, and the average Rank-1 recognition rate of faces with all angles under the pitch of CAS-PEAL-R1 dataset is 99.03%, which can effectively solve the frontalization problem of multi-angle side faces.

The traditional environmental sound classification model does not fully extract the features of environmental sound, and the full connection layer of conventional neural network is easy to cause over-fitting when the network is used for environmental sound classification. In order to solve the problems, an environmental sound classification method combining with Mel-Frequency Cepstral Coefficient (MFCC), deep convolution and Bagging algorithm was proposed. Firstly, for the original audio file, the MFCC model was established by using pre-emphasis, windowing, discrete Fourier transform, Mel filter transformation, discrete cosine mapping. Secondly, the feature model was input into the convolutional depth network for the second feature extraction. Finally, based on reinforcement learning, the Bagging algorithm was adopted to integrate the linear discriminant analyzer, Support Vector Machine (SVM), softmax regression and eXtreme Gradient Boost (XGBoost) models to predict the network output results by voting prediction. The experimental results show that, the proposed method can effectively improve the feature extraction ability of environmental sound and the anti-over-fitting ability of deep network in environmental sound classification.