Technologies on Display
G3 - Automatic Face Recognition
(1) Automatic Face Recognition
to growing demands in areas such as law enforcement identification, banking
and security system access authentication, and anti-terrorist video
surveillance, automatic face recognition has attracted great attention in
recent years. Comparing to other biometrics technologies such as fingerprint
and iris recognition, face recognition is the best suited for surveillance
of busy public places like airports and the customs, since it does not
require those being watched to cooperate by looking into an iris scanner or
putting a hand on a fingerprint reader. It is the only biometrical
technology that does not require those being checked to cooperate. Face
recognition devices can work with the video feeds from the cameras that are
already ubiquitous in public places. It is also much easier for authorities
to obtain a suspect's photo than to obtain his other biometrical
identifiers. We recently developed a new face graph model based on discrete
wavelet transformation. The method can locate key facial features such as
nose tip and mouth corner at a rate a hundred times faster than the
traditional elastic graph. In addition, we established a unified framework
for the popular subspace based face recognition approaches. A unified
subspace analysis algorithm is developed under this framework that gives
higher recognition accuracy than all the traditional subspace methods.
Home security, anti-terrorist, airport, and casinos.
- Access control: Bank, private company, and network security.
- E-commerce: Fraud control, ATM, and internet banking ID.
- Law Enforcement: Duplicate ID detection and mugshot database search.
- Government: Customs control, social security service, motor vehicle
registration, and immigration.
- Multimedia: Information retrieval, video compression, and
- Other: Finding lost relatives and personalized service.
- No touching of any equipment.
- Does not require those being checked to cooperate.
- Easy to correct error and easy to obtain data.
- Fast computation using discrete wavelet transform features.
- Robust to lighting and expression changes.
(2) Face Sketch Recognition and Face
important application of facial recognition is to assist law enforcement.
For example, automatic retrieving of photos of suspects from police mug-shot
databases can help police narrow down the number of potential suspects
quickly. However, in most cases, only the drawing based on the recollection
of an eyewitness is available. Therefore searching an image database by
using a sketch drawing is definitely useful. Despite the great need of such
an automatic photo retrieval system using face sketches, limited research
has been carried out in this area. We have developed a unique face sketch
recognition system that achieves a higher recognition performance than human
beings. Given a simple sketch of a person, the system can potentially search
through hundreds of thousands of photos to hunt down the target person. Such
a facial recognition system capable of automatic classification of both face
photos and drawn sketches can be a crucial investigator support tool for
practical law enforcement in any police departments and national security
agencies around the world.
video surveillance, the faces of interest are sometimes small in size
because of the great distance between the camera and the target people. As
such many detailed facial features are lost in the low-resolution images.
For identification, especially by human, it is useful to render a
high-resolution large face image from the low-resolution one. This technique
is called face hallucination. Using the special property of face structure,
we develop an effective algorithm that can learn from a training set
containing high- and low- resolution image pairs. The reconstructed photo by
our hallucination algorithm appears realistic when compared to the original
- Law Enforcement
- Sketch generation for entertainment application
- Video Surveillance
- The first and only working system for sketch recognition.
- Better performance than human being
- Simple and fast algorithm
Prof. XiaoOu Tang
Department of Information Engineering