کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
395110 665929 2012 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An adaptive classification system for video-based face recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
An adaptive classification system for video-based face recognition
چکیده انگلیسی

In many practical applications, new information may emerge from the environment at different points in time after a classification system has originally been deployed. For instance, in biometric systems, new data may be acquired and used to enroll or to update knowledge of an individual. In this paper, an adaptive classification system (ACS) is proposed for video-based face recognition. It combines a fuzzy ARTMAP neural network classifier, dynamic particle swarm optimization (DPSO) algorithm, and a long term memory (LTM). A novel DPSO-based learning strategy is also presented for incremental learning of new data with this ACS. This strategy allows to cojointly optimize the classifier weights, architecture, and user-defined hyperparameters such as classification rate is maximized. Performance of this system is assessed in terms of classification rate and resource requirements for incremental learning of data blocks coming from real-world video data bases. The necessity of a LTM to store validation data is shown empirically for different enrollment and update scenarios. In addition, incremental learning is shown to constitute a dynamic optimization problem where the optimal hyperparameter values change in time. Simulation results indicate that the proposed system can provide a significant higher classification rate than that of fuzzy ARTMAP alone during incremental learning. However, optimization of ACS parameters requires more resources. The ACS needs several training sequences to produce the optimal solution, and adapting fuzzy ARTMAP parameters according to classification rate tends to require more category neurons and training epochs.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 192, 1 June 2012, Pages 50–70
نویسندگان
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