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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/342

    Title: 基於FPGA單晶片及模糊語意影像特徵表示法 之車內人臉辨識;Driver’s Face Recognition Using FPGA Chip and Semantics-based Vague Image Representation
    Authors: 徐育晨;SYU, YU-CHEN
    Contributors: 電機工程研究所
    Keywords: 人臉辨識;現場可程式邏輯閘陣列;模糊語意影像特徵表示法;嵌入式系統;Face Recognition;Field Programmable Gate Array;Semantic-based Vague Image Representation;Embedded System
    Date: 2016
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 在環境智能(Ambient Intelligence)的系統發展裡,人臉辨識是身分驗證之重要技術之一,在許多重要的公共場合及居家環境,此技術都可以被用來當作人員及安全掌控的重要工具。而反觀在智慧型運輸系統(Intelligent Transport System, ITS)上面,此類系統卻還尚未普及。其主要原因是車內空間有限,所以搭載的處理器運算能力往往無法達到複雜之快速人臉辨識運算。因此,本研究特地針對汽車,開發一套基於單晶片設計之快速人臉辨識系統,並且達到低功耗、低開發成本之效益。在此研究裡,本人首先利用一小型數位相機,對駕駛人臉進行擷取,接著以單一現場可程式邏輯閘陣列(Field Programmable Gate Array, FPGA)晶片,以硬體電路設計架構實現快速影像辨識功能。而為了要達到最少之晶片邏輯閘使用量而又同時兼顧高速處理能力,本研究還特地採用了修正型之模糊語意影像特徵表示法(Semantic-based Vague Image Representation, SVIR)來完成人臉特徵之表達及擷取。此特徵擷取演算法特色在於不需要複雜之幾何、矩陣公式、及三角函數,就能夠快速的對物體外型以語意的方式進行有效率的描述,並有利於後續人臉特徵之分類,因此特別適用於小型崁入式系統(Embedded sysytem)設計當中。根據本研究的實驗結果,車主的辨識率大多落在87%-93%之間,並且能與陌生對象的辨識率有明顯的差距存在。此外,當本研究將人臉辨識之門檻值設為85%時,可以使得本研究擁有最低的辨識錯誤率,以達到人臉辨識之可靠度之需求。而本系統之設計亦可運用在智慧型居家的門禁系統以及智慧型手機的解鎖系統上。
    The face recognition technology is an important technology for identification in the Ambient Intelligence systems. Such technology can be employed in public occasions and smart homes. However, for the intelligent Transport System (ITS), it is still not popular due to the limited interior space of car, which leads to a weak computing system. Therefore the goal of this project is to develop a real-time face recognition based on single-chip design for the vehicle, and it will achieve low-power consumption and low-cost production. In this project, this study firist uses a digital camera to extract driver’s face and then uses Field Programmable Gate Array (FPGA) to implement image recognition in real-time. To achieve a minimum usage of logic element and high-speed processing capability, this research particularly adopts the algorithm of Semantic-based Vague Image Representation (SVIR) to perform the representation and extraction of facial features. The characteristic of this feature extraction algorithm is that can efficiently describe object’s contour by semantics and does not need to employ complicated geometry, matrices, and trigonometric funtions. Thus it is useful for facial classification, particularly suitable for miniature embedded system. Finally, according to the experimental results of this project, the similarity rate of faces are mostly among 87% and 93%, which distinguishes from strangers. In addition, when the similarity threshold of face recognition is set for 85%, it can make the system achieves the lowest recognition error rate and meets the reliability requirement of face recognition. The design of the system is promising and can also be used in intelligent home access control systems and smart phone unlock system in the future.
    Appears in Collections:[電機工程研究所] 學位論文

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