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標題: 應用於嬰幼兒監護系統之臉部表情辨識與異物偵測演算法設計與實作
Design and Implementation of Facial Expression Recognition and Foreign Object Detection Algorithm for Baby Watch and Care System
作者: 游聖民
Yu, Sheng-Min
關鍵字: Face Detection;人臉偵測;Facial Expression Recognition;Object Detection;表情辨識;異物偵測
出版社: 電機工程學系所
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Cohn, “ Eye-state action unit detection by gabor wavelets,” In Proceedings of International Conference on Multi-modal Interfaces, pp. 143–150, 2000. [27] P. Ekman and W. V. Friesen, “The Facial Action Coding System: A Technique for The Measurement of Facial Movement,” San Francisco: Consulting Psychologists Press, 1978. [28] Y. Tian, T. Kanade, and J. F. Cohn, “Evaluation of Gabor-Wavelet-based Facial Action Unit Recognition in Image Sequences of Increasing Complexity,” International Conference on Face and Gesture, pp. 218-223, 2002. [29] Y. Tian, T. Kanade, and J. F. Cohn, “Recognizing action units for facial expression analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 97-115, 2001. [30] T. Kanade, J. F. Cohn, and Y. Tian, “Comprehensive database for facial expression analysis,” Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46-53, 2000. [31] J.-J.J. Lien, T. Kanade, J. F. Cohn, and C. C. 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最後將此嬰幼兒監護系統實現於ARM926EJ-S CPU與Xilinx FPGA之嵌入式系統平台上。並基於軟硬體協同設計的概念,抽出演算法裡運算複雜度最高的模組來做成硬體加速IP。當ARM CPU操作於266MHz且系統頻率為50MHz時其純ARM程式碼版本的影格率(Frame rate)大約可達每秒3.03張,而軟硬體共設計版本的影格率也可到每秒1.86張。

In this study, we will discuss a digital intelligent baby-watch-and-care system that can recognize baby''s expression and detect the external object. The system will alert watchers when it detects something around mouth and nose, and a vomit condition. In addition, we will figure out whether babies are within the safe condition by baby facial expressions. Thus, we can replace the manpower security with the intelligent video system and reduce the watcher''s burden.

In the intelligent baby-watch-and-care system, there are two subsystems, which include the facial expression recognition and external object detection. On the part of facial expression recognition, there are three conditions, which include deadpan, smiling, and crying. First, we extract baby's face features from the image. The features distance will be calculated by the features and they will be as input values to the neural network system. Thus, the scheme can recognize baby facial expressions. On the other part of the external object detection, we focus on detecting the vomit and something around mouth and nose. In order to achieve the above-mentioned demand, we can observe the color change which is near to the mouth by detecting the current and previous frames in dynamic video sequences. The experiment results show that our algorithm can detect the eye features accurately, and the accuracy for the eye feature detection is up to 88%, and the processing time needs 45 ms, then the accuracy for the facial expression recognition is about 80% by using the Quadcore (@2.66GHz) computer with C codes.

Finally, by following the principle of the HW/SW co-design, the baby- watch-and-care system is implemented on an embedded platform which is composed of ARM926EJ-S CPU and Xilinx FPGA. First, we profile the execution time of each module in the algorithm, and choose the maximum computational complexity module for the hardware realization. The HW/SW co-design can process 1.86 frames per second, and the pure software design with the ARM CPU can achieve 3.03 frames per second when the ARM CPU operates at 266MHz and the system frequency operates at 50MHz.
其他識別: U0005-2707201016585600
Appears in Collections:電機工程學系所

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