International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:13 No:01 15 138601-7575-IJVIPNS-IJENS © February 2013 IJENS I J E N S Abstract -- This paper deals with the analytical-statistical method for the face recognition. Although the bases of the face recognition are known by researchers worldwide, the statistical tests of data obtained by measuring chosen anthropometrical points can be found in several articles. Our aim is to show how the data act during the various emotions of one face, which will be helpful for deeper knowledge of how the face behaves. The focus is also aimed at study of aging. The data were tested in the STATISTICA software using e.g. the ANOVA, the Shapiro-Wilk test, the k-Means clustering and the t-test. Acquired results reflect the difficulty of describing the face and the applicability of combination of different recognition methods (e.g. methods based on neural networks, recognition of facial contours, distribution of the gray scale in the image, deformation models) to get the best results in the verification/identification of a human. Index Term-- pattern recognition, face recognition, statistical methods, biometrics I. INT RODUCT ION The face recognition is one of the most utilized technologies for protection of assets. The roots of this technique lie in the anthropology, respectively in the judicial anthropometry by A. Bertillon. As can be apparent this method from the 19 th century evolved into criminalistic portrait identification (also called photo-anthropometry). This attitude was utilized mainly in the 1960s with the advancement in analytical-statistical and graphical methods for identification of a person. This paper is focused on analytical-statistical method, which was mainly used in the 1970s, and is a representative of one of the simplest methods of today’s techniques . The obtained information is characterized by the space and linear structure of the face, which characterized the individuality. The scientific analysis proved that only 12 basic anthropometrical points are quite This work was supported by the Internal Grant Agency at Tomas Bata University in Zlín, project No. IGA/FAI/2013/001, and by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. K. Sulovská is with Tomas Bata University in Zlín, Faculty of Applied Informatics, Department of Security Engineering, nám. T. G. Masaryka 5555, 76001 Zlín, the Czech Republic (phone: +420 57 603 5133; fax: fax: +420 57 603 2717; e-mail: sulovska@fai.utb.cz). S. Bělašková is with Tomas Bata University in Zlín, Faculty of Applied Informatics, Department of Mathematics, nám. T. G. Masaryka 5555, 76001 Zlín, the Czech Republic (e-mail: belaskova@fai.utb.cz). M. Adámek is with Tomas Bata University in Zlín, Faculty of Applied Informatics, Department of Security Engineering, nám. T. G. Masaryka 5555, 76001 Zlín, the Czech Republic (e-mail: adamek@fai.utb.cz). adequate for this characterization (Fig. 1) [14]. As the anthropometry is a three-dimensional measurement, the photo- anthropometry deals with the two-dimensional photographs, which can bring serious difficulties [9], [10], [11], [12]. One of the limiting factors is the quality and the angulation of the photograph [16]. Fig. 1. Extended number of anthropometrical points (marked order meets [14]) When focusing on statistical approaches to the face recognition, especially focused on emotional changes in faces, only the machine recognition is disposable [1], [2], [5], [6], [7], [13], [18], [19]. This type of recognition shows the utilization of the bases made by the photo-anthropometry in the way enriched by the novel computational methods [8], which raises the accuracy of verification/identification. Although 12 points are enough, we may add extra points adapted to the image. Such points need to be clearly visible in the image. Features like hair features or hairlines, which can be easily modified, should be omitted as they are unreliable and unpredictable for computerized methods. Features like face shape have higher rates of interobserver agreement, and pronounced ear projection is said to be the best discriminators [3], [4], [15], [16]. The resolution of processed image for the use in police-court (forensic) applications is set to be standardly 500 dpi (e.g. used by FBI also for fingerprint Study of Face Recognition Using Statistical Analysis Kateřina Sulovská, Silvie Bělašková, Milan Adámek