Face Detection

Face detection aims to find all faces in an image or video sequence in a sense that locates their positions and gives their approximate sizes and poses as well.
   Face detection task can be divided into two category according to pose changes of off-image-plane rotation, that is, frontal face detection and multi-view face detection. In addition taking in-image-plane rotation into consideration, it can be further extended to rotation invariant frontal face detection or rotation invariant multi-view face detection.

Face Alignment

Face alignment is a procedure to get the shape of a human face. Here, the shape means all the landmarks used to represent a face.
    Face alignment can make the face distribution statistically more compact and provide a good base for face recognition, face modeling and synthesis.

Face Pose Estimation

Face pose classification as a simplified case of face pose estimation is very important in many face processing work including face modeling and face recognition. In general, face pose may represent human physical state, such as sleepiness and concentration. Therefore, it is very important in many real-life applications, such as monitoring attentiveness of drivers or automating camera management.
  We extract pose related information by pose apperance models, which are built by statistical learning over a very large training set. Given two eye-centers and mouth center, each face region is warped to all potential pose models. The face pose can be estimated by maximizing the fitness of the face to the model.

Face & Head Tracking

Visual cues designed for general object tracking problem hardly suffice for robust head tracking under diverse circumstances, making it a necessity to utilize higher level information which is object-specific. To this end we use the vector-boosted multi-view face detector as the “face cue” in addition to other general visual cues targeting the entire head. Data fusion is done by an extended particle filter which supports multiple distinct yet interrelated state vectors (referring to face and head in our tracking context). Furthermore, pose information provided by the face cue is exploited to help achieve improved accuracy and efficiency in the fusion. Experiments show that our algorithm is robust against target position, size and pose change as well as unfavorable conditions such as occlusion, poor illumination and cluttered background.

Face Organ State Estimation  and Face Organ Contour Extraction

Face organ state estimation aims to recognize the state of eye and mouth (close or open), with a real value indicate the degree of openness. we have explored classical methods in machine learning area such as LDA, Bayes inference, SVM, Adaboost, etc. On the other hand, Face organ contour extraction aims to extract the contour of eye and mouth. Our extraction algorithm is implemented under the framework of Active Shape Model.

Face Recognition

Face recognition aims to identify or verify one or more persons in an image or video using a stored database of faces.  We  focus on face recognition in uncontrolled environments, such as faces in consumer image collections and faces in the news or teleplay videos. We are developing statistical as well as instance-based methods to improve recognition accuracy. Also, we are interested in developing improved models for better performance on very large galleries. Moreover, we are trying to develop better face representations  to deal with variations of illumination and expression. 

Face Demographic Classification including Gender, Age, Ethnicity, etc. 

Demography classification aims to tell gender, ethnic and age information of face images automatically. We are developing demographic information image filters to extract gender, ethnic and age information from consumer images.


Face Indexing and Retrieval

Face index and retrieval try to sort and retrieve face images and videos by their face content automatically. We are developing tools for the managements of image or video collections such as digital family photo albums or home videos, teleplays that mainly  involve only a dozen of people or so.


Human Detection and Tracking

Human detection aims to locate all humans in a given image, usually in the form of bounding rectangles. While human tracking aims to find all human trajectories in a video, usually in the form of sequences of bounding rectangles. We are trying to develop robust and efficient algorithms to detect humans and to track human movements.




Other Researches

3D Face Modeling and Reconstruction

Face Expression Recognition

Human Pose/Gesture Recognition