With the high-speed development of computer technology and the need for video monitoring applications

face detection and tracking are gradually becoming a research hotspot and are widely used in public security

intelligent video surveillance

authentication

and others. Considering factors such as variation in lighting conditions

target obscuration

and long-term tracking

tracking human faces produces various errors that reduce the performance of the entire system. To resolve these problems

this paper presents an approach with a combination of detection and tracking technologies

and involves the three modules of detection

control and tracking (DCT). In the control modules

human face features are detected though haar features

and calculated with integral images. This reduces computational complexity and enhances system speed. The AdaBoost algorithm is then used to extract a face’s information in the detection module. Next

the information is transferred to the tracking module. The face target is tracked by using online random fern and Speeded Up Robust Features(SURF) algorithms in the tracking module. To improve tracking speed

2-BitBP features are described in the random fern. Then

the P-N learning method is used to quickly locate similar objects. Similar targets and matching confidence of the target are determined based on the NCC algorithm. The similar target with the largest confidence value is considered the tracking target. Whereas

the control module is used to judge accuracy of the current tracking after each target is detected. The interactive unit in the control module transfers the testing results into the tracking modular for subsequent tracking. Three filters in the check unit are designed to filter check results in terms of scale size

target position

and similarity. This balances the performance of detection module and tracking module and avoids tracking failure because running the system for a long time and interference of similar targets. By using international standard data sets

the experimental results compared with LBP + Camshift + Kalman filter algorithm

the SEMI-supervised learning algorithm

and the Tracking-Learning-Detection (TLD) algorithm

show that the DCT approach proposed in this paper has good tracking effect on large scale target changes

occlusion

rotation

deformation and illumination changes. This effect can be achieved at the level with identification accuracy of over 95%

the average error recognition rate of 0.86% and missing recognition rate of 0.78%. On tracking accuracy

when light varies greatly

the experiments of the target center offset show that the DCT approach can still track the target position accurately. In terms of tracking performance

the average running time of this approach is 46.25 ms

and its effective rate is 98.25%. DCT can eliminate error accumulation

and automatically detect the target once tracking fails. Moreover

it can reduce the influence of environment lighting

obscuration

and affine transformation and meet performance requirements of real-time tracking in the augmented reality system. This approach can improve real-time performance and robustness of the system when applied to a video face tracking system.