Temporal Correlation and Probabilistic Prediction Based Face Detection Framework in Real Time Environment

Conventionally, the object detection algorithm proposed by Viola Jones (using Haar-like features, Integral image and AdaBoost algorithm) is implemented in majority of the face detection applications. In this article, an improvement to the application of Viola Jones algorithm in real time environment is presented by exploiting the analogy between video motion estimation and continuous object detection. Using the temporal correlation between successive frames of a real time input, various modifications improving the robustness and computational complexity of face detection are proposed. The proposed method focuses on reducing the search area for face detection on the basis of probabilistic prediction. In addition, approximation of minimum face size renders an improved performance. The modified Face Detection Framework (FDF) is applied in two scenarios, canned video sequences from public databases and real time inputs from a low resolution camera, yielding improved results in both the cases.