I 1 I 2004 International Conference on Image Processing (ICIP) ONLINE FACE RECOGNITION SYSTEM FOR VIDEOS BASED ON MODIFIED PROBABILISTIC NEURAL NETWORKS Jun Fan. Nevenka Dimitrova and Vasanth Philomin sort the candidate by the confidence make post-processing easicr. Philips Research USA Briarcliff Manor, NY 10510 junfan~~ee.colutiibia.edu, nevenka.dimitrova@philips.com ABSTRACT Video relricval in consumer applications dcmands high lcvel scmantic descriptors such as people’s idcntity. The problem is that in a variety of videos such as home vidr.os. Hol!wvaod content. TV hi-oudcast content, mobile phone videos firces ore not e~i.~.v to recognize. Even more. a closed svstem trained to recognize on!v a predeterniined numher of’f2ace.s will become obsolete very easily We dcveloped an online-learning Peace recognition system for a variety of videos based on Modified Probabilistic Neural Networks (MPNN). This face recognition system can detect and recognize known faces, as wcll as automatically detect unknown faces and train the unknown faces online into ncw facc classilicrs such that this “unknown face” can be rccognizcd if it appears again. MPNN is a variant of the PNN with thresholding on the category (output) layer of a Probabilistic Neural Network (PNN) in order to detect unknown categories of input data. The PNN training makes the online training very fast because adding new faces does not require retraining of the known categories. Our experimental results show that on-line learning gives somewhat lower hit rate, while at the same time reducing the false positive rate. 1. INTRODUCTION Most face recognition systems are trained on a fixed number of faces that are known in advance [1][2][3]. These systems will only recognize the faces with known models and the face database cannot be updated during the classification procedure. For example, they will work for surveillance systems, which have to recognize all employees of a company and alert to any intruders, or an airport surveillance system that is trained to recognize known terrorists. However, in the area of home video, TV broadcast video, wearable video, in addition to the known people there is a need to recognize the new people appearing with each new video. In home videos for example, if a system is trained to recognize only family members then a visitor is labeled as “other” or “unknown”. Of course there are travel videos with many new faces that are transient. A system that categorizes images and videos based on people presence has to distinguish all these categories of important and measurement, which Figure 1. Face Recognition System bchitecture During the training phase, the system reads face examples for each face (actodcharactei) and trains the Probabilistic Neural Networks (PNN) [4][6] based on the features of these faces. We ,choose Vedtor Quantization 0-7S03-8554-3/04/S20.00 0 2 0 0 4 IEEE. 201 9