Face Recognition Performance Comparison Between Real Faces and Pose Variant Face Images from Image Display Device
Face recognition technology, unlike other biometric methods, is conveniently accessible with the use of only a camera. Consequently, it has created an enormous interest in a variety of applications, including face identification, access control, security, surveillance, smart cards, law enforcement, human computer interaction. However, face recognition system is still not robust enough, especially in unconstrained environments, and recognition accuracy is still not acceptable. In this paper, to measure performance reliability of face recognition systems, we expand performance comparison test between real faces and face images from the recognition perspective and verify the adequacy of performance test methods using an image display device.
KeywordsFace recognition Image display device Performance evaluation
Face recognition is a widely used biometric technology because it is more direct, user friendly, and convenient to use than other biometric approaches. Face recognition technology is now significantly advanced, has great potential in the application systems. However, it is difficult to guarantee of performance due to insufficient test methods in real environment. The best method is direct evaluation from human subjects in real environment. Unfortunately, in this case, it would be considered impossible to consistently obtain the same way for a lengthy period of time a certain number of persons. That is, it’s difficult to guarantee objectivity and reproducibility.
There are many approaches for performance evaluation of the face recognition in the system level including methods using an algorithm , a mannequin , and a high-definition photograph . The first method simply evaluates the performance of an algorithm installed in a face recognition system. However, the performance of an algorithm cannot guarantee the performance of a face recognition system. The second method uses mannequin instead of real human face. This method has a number of problems because the material coating the mannequin is not the same as human skin. Last, the method using a high-definition photograph has overcome some of the existing problems. However, it still experiences minor difficulties with automatic control interoperation with a computer, and a lack of reproducibility in real situations.
In this paper, we expand performance comparison test between real faces and face images from the recognition perspective and verify the adequacy of performance test methods using an image display device. The paper is organized as follows: in Sect. 2, we explain limitation of precious works. Section 3 describes how to construct the facial DB. In Sect. 4, we show and analyze the experimental results. Section 5 concludes this paper.
2 Previous Works
In the previous works, we have introduced performance evaluation method of face recognition using face images from a high definition monitor and prove similarity between real faces and face images [4, 10]. However, the previous work has a limitation to reflect performance in real environments as it is a test only using frontal pose images.
3 Facial DB
The majority of facial images used to evaluate face recognition algorithms such as Feret , PF07 , and CMU PIE  could be used for the proposed test method. However, most images are not adequate because of the low-resolution output of the image display device. To overcome this challenge, high-resolution facial DB was required.
Face recognition rate(%)
In this paper, we expand the previous works and verified the similarity of real face and face images from an image display device by comparing face recognition performance changes according to pose. Based on the comparison results using an image display device, the proposed method can be applied to the face recognition performance evaluation in system level.
This work is partly supported by the R&D program of the Korea Ministry of Trade, Industry and Energy (MOTIE) and the Korea Evaluation Institute of Industrial Technology (KEIT). (Project: Technology Development of service robot’s performance and standardization for movement/manipulation/HRI/Networking, 10041834).
- 1.TTAK.KO-10.0418, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 1 Performance Evaluation of Recognition Algorithm (2010)Google Scholar
- 2.TTAK.KO-10.0419, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 2. System Level Performance Evaluation using Human Model (mannequin) of Human Face Recognition (2010)Google Scholar
- 3.TTAK.KO-10.0507, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 3. Performance Evaluation of Face Recognition using Face Photos (2011)Google Scholar
- 4.Cho, M.Y., Jeong, Y.S., Chun, B.T.: A study on face recognition performance comparison of real images with images from LED monitor. J. Inst. Electron. Eng. Korea 50(5), 1164–1169 (2013)Google Scholar
- 6.Lee, H., Park, S., Kang, B., Shin, J., Lee, J., Je, H., Jun, B., Kim, D.: The POSTECH face database (PF07) and performance evaluation. In: Proceedings IEEE International Conference Automatic Face & Gesture Recognition, pp. 1–6 (2008)Google Scholar
- 8.ISO 15076-1:2010, Image technology colour management – Architecture, profile format and data structure – Part 1: Based on ICC.1:2010 (2010)Google Scholar
- 9.IEC 61966-2-1:1999, Multimedia systems and equipment – Colour measurement and management – Part 2-1: Colour management (1999)Google Scholar
- 10.Cho, M.-Y., Jeong, Y.-S.: Face recognition performance comparison of fake faces with real faces in relation to lighting. J. Internet Serv. Inf. Secur. (JISIS) 4(4), 82–90 (2014)Google Scholar
- 11.Phillips, P.J., et al.: Face recognition vendor test 2002. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2003. IEEE (2003)Google Scholar