Face recognition system on Raspberry Pi Olegs Nikisins, Rihards Fuksis, Arturs Kadikis, Modris Greitans Institute of Electronics and Computer Science, 14 Dzerbenes Street, Riga, LV 1006, Latvia Abstract An embedded face recognition system based on the Raspberry Pi single-board computer is proposed in this paper. Face recognition system consists of face detection and face localization using Haar feature-based cascade classifier. Face features are extracted using weighted Local Binary Pattern algorithm. Developed system performs one full face analysis in 110 ms. Comparison of two biometric samples is performed in 2 ms. The proposed embedded face recognition system was tested on FERET database and achieves accuracy of CMC: 99.33% and EER: 1%. Keywords: Face recognition, Raspberry Pi, Local Binary Pattern. 1. Introduction Real time human identification systems are important for security, surveillance and biometric applications. Usually it is desirable to detect, track and recognize persons in public areas such as airports, shopping centres, in areas with restricted access such as private offices, houses etc. Human identification can be performed by analysing its biometric information, such as fingerprints, face, iris, palm prints, palm veins etc. However, for fast and convenient person recognition, still the most suitable biometric parameter is facial information. Identification of humans by using facial biometrics is still challenging task, due to the variable illumination, changing facial expressions according to mood changes, head orientation and pose. Over the years, various face detection algorithms have been developed. Some face recognition methods analyse the geometric features of facial images, such as location and distance between nose, eyes, and mouth [5] [3]. However, these methods are sensitive to the changes in illumination and facial expression. Because of this drawback, most of the face recognition systems try to extract some holistic features from the original face images for matching. By using holistic methods face is recognized using descriptions based on the entire image rather than on local features of the face [6]. Many subspace learning based holistic feature extraction methods have been developed, including Eigenfaces [12], Fisherfaces [2], 2D PCA [14] and others. In this paper we describe the holistic method called local binary pattern (LBP) [9]. In this paper we propose an embedded face recognition system that can be used as a system to control electromagnetic door lock of the doors, to recognize persons at boarder or elsewhere.