Face detection is constantly an active research subject in computer vision and pattern recognition. Face detection is also a constituent part of pattern recognition

artificial intelligence

information security

and many other disciplines. With video network coverage widely increasing in recent years

face detection has been increasingly used in the field of video surveillance. However

many factors require consideration in face detection

such as the complex environments

multiple faces

and face rotation angles. In view of these interference problems in nonideal condition

a cascaded neuron network based on a multi-layer probability state-restricted Boltzmann machine (P-RBM) is proposed in this study to overcome the challenge of accurately and rapidly detecting faces. The neurons of RBM only have two states

namely

activated and nonactivated; this state mode can inhibit the interference in the learning result induced by the inadequate active information

while it simultaneously increases the likelihood that the learning network falls into a local optimum caused by the shielding of relatively weak information. To solve this contradiction

the proposed method uses the probability state of neurons in RBM as their activation degree

which better models the activity state's continuous distribution of the neurons in the human brain. Using the probability state not only retains the weak active information but further decreases the effect caused by the former layer's miscalculation. Simultaneously

this method simulates the hierarchical learning mode in the human brain by cascading multiple P-RBMs. This cascaded network can achieve multi-layer nonlinear mapping and obtain the semantic feature of the input date by extracting the input data's separate level features. Furthermore

this cascaded network can learn the relationship hiding within the data to make the learned features be more promotional and expressive. Simultaneously

the number of the hidden layer's neurons decreases layer-by-layer to control the network's scale and enhance the robustness. Finally

the proposed method uses the layered training and the entire optimization to balance robustness and accuracy. The greedy layer-wise learning is used in the layered training to avoid the training error transferring in layers

thereby solving the problem of the multi-layer network easily falling into the local optimum. Furthermore

a preprocessing layer is used to detect the skin color area to reduce the number of neurons in the detection network and speed up the detection speed. Testing the single face detection performance in the LFW and FERET

the proposed method nearly achieves entirely accurate detection. Testing the video face detection in the PKU-SVD-B database

the missing detection rate and the false detection rate of the proposed method are all lower than that of the state-of-the-art methods

such as Adaboost and Adaboost combined with skin color detection

and its detection speed is faster. Moreover

the proposed method has a good detection performance for the face with a large rotation

which is tested in the CAS-PEAL database. Experimental results show that regardless of whether a static single face or video multi-face detection occurs under complicated conditions

apart from the faster detection speed and robustness against face rotation

the proposed method possesses lower false detection rate and lower missing detection rate. Aiming at the multi-face detection based on skin color

this method can significantly reduce the false detection rate.