Visible to the public Probability Boltzmann Machine Network for Face Detection on Video

TitleProbability Boltzmann Machine Network for Face Detection on Video
Publication TypeConference Paper
Year of Publication2020
AuthorsYE, X., JI, B., Chen, X., QIAN, D., Zhao, Z.
Conference Name2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Date PublishedOct. 2020
ISBN Number978-0-7381-0545-1
KeywordsDeep Learning, deep learning network, deep video, Face detection, face recognition, feature extraction, greedy layer-wise learning, Metrics, Neurons, pre-training, pubcrawl, resilience, Resiliency, Scalability, Training, video face detection, visualization

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer's neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.

Citation Keyye_probability_2020