Rotation Invariant Multiple Face-detection Architecture for Smart TV

Rotation Invariant Neural Network-Based Face Detection

The system (see Figure 2) uses a neural network, called a “router”, to analyze each window of the input before it is processed by a “detector” network. If the window contains a face, the router returns the angle of the face. The window can then be “derotated” to make the face upright. The derotated window is then passed to the detection network, which decides whether a face is present. If a non...

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated | the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under di erent viewin...

Rotation Invariant Real - time Face Detection and Recognition System

Parallelized Architecture of Multiple Classifiers for Face Detection

This paper presents a parallelized architecture of multiple classifiers for face detection based on the Viola and Jones object detection method. This method makes use of the AdaBoost algorithm which identifies a sequence of Haar classifiers that indicate the presence of a face. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing of...

Invariant Local Features for Face Detection

In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the detection of generic face features and a...