Discussion

For face detection, Table 1 shows that the detection rate of our face detector is more than eighty percent, and it is sufficient for a practical entrance system. The training and testing data are all frontal faces which contains many faces of eastern people.

However, there are still some situations that may cause the system fail. Figure 4-14 and Fig. 4-15 are the examples of system fail. Sometimes the shape and texture of non-faces is too close to the truly faces, the faces are non-frontal faces or partial occlusion or too small to be detected.

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Fig. 4-14: Examples of system fail #1

Fig. 4-15: Examples of system fail #2

For face identification, Table 3 shows that the detection rate of our face identifier is more than ninety percent for the five training images for each person, and it is enough for many face identification applications. The training and testing data all consist of five images of different head orientations.

There are some situations making the system fail, such as false acceptance and false rejection. False acceptance means the impostor is identifying as the client by the face identifier; oppositely, false rejection means the client is identifying as the impostor. Figure 4-20 is an example of false acceptance , the impostor (Fig. 4-16(a)) is identified as the client (Fig. 4-16(b)).

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(a)

(b)

Fig. 4-16: Examples of system fail #3: (a) the impostor (b) the client

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Chapter 5

Conclusions and Future Work

In this thesis, we present a system for the multi-client identification using adaptive probabilistic model (APM). The design of this system is based on the principles of robustness and practicability.

For face detection, the technique of lighting normalization using histogram fitting is applied to improve the performance of face detector., and a region-based clustering method is proposed to deal with the problem of multi-candidates around the faces The experimental results show that the process of lighting normalization can actually improve the detection rate of face detector.

For face identification, adaptive probabilistic model (APM) is introduced to model the characteristic of the clients. According to the design of APM, the system can on-line register new clients and on-line update the information of the clients. The APM is composed of five images of different head orientations for each person. By the process of adaptive updating, the weights of five different poses and the matched probabilistic function are adjusted to adapt the latest information of registered clients..

The experimental results show that the proposed APM technique actually has the good performance for face identification.

To further improve the performance and the robustness of our system, some enhancements can be done in the future:

(a) A robust face detector is necessary for a practical face recognition system. If the faces are not extracted from the images correctly, the face identifier cannot work in the following process. For our system, one of the restrictions is that we cannot handle the variant poses of non-frontal faces, such as the

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side faces. It is probably to select the variant poses of non-frontal faces be the training sets to solve this problem.

(b) It is hard to identify the face if the variation of the rotated angle for the face is too big. Before the process of face identifier, we may employ a method of face calibration to deal with this problem. Furthermore, this method of face calibration can also use in face detector.

(c) For face identification, a threshold is used to discriminate between the clients and the impostors. There is a trade-off problem on the selection of the threshold. A big threshold can prevent an imposter being identified as a client, but it also increases false rejections of clients. There are two possible solutions for this problem: one is to increase the specificity of face model, the other is to apply feature extraction methods which can extract more distinguishable features than eigenfaces.

(d) The tolerable degree of the proposed system may not be affordable for requirements of large numbers of clients, for instance, an entrance guard system for a big company with hundreds of staff. Hence the tolerable degree of the identification system should be improved in the future.

(e) This work lacks quantitative results of the whole face recognition system that consist of face detection and face identification. In the future work, this kind of experiments will be designed and implemented.

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