人體的生物特徵可區分為生理特徵和行為特徵，生理特徵如：臉部、指紋、靜脈、掌紋、骨骼、虹膜等，屬於靜態的特徵，行為特徵如：簽名、聲紋、心跳、步態等，則屬於動態的特徵，這些生物特徵都具有個體的唯一性、穩定性，因此，可利用這些特徵作為判別個人身分的依據，發展出各式的生物辨識技術。同時，隨著資訊科技與網際網路的發展，行動裝置的已經非常普及，且應用的範圍廣泛，個人資訊安全更顯得重要，使得生物辨識技術結合行動裝置在安全驗證上受到重視。
這些生物辨識技術中，人臉辨識最大的特點就是自然性和非接觸性。自然性指的是和人類進行個體辨識時所利用的生物特徵相同，例如：人臉辨識是通過觀察比較人臉對其他個體進行區分和確認身分。除此之外，使用人臉作為生物特徵辨識擁有取得容易，不用接觸即可完成的優點，故成為主流的生物辨識方法之一。
本論文在人臉偵測部分是使用哈爾特徵篩選出人臉的影像，再以尺度正規化、旋轉正規化、亮度正規化對影像的進行前處理，並使用卷積神經網路(Convolutional Neural Network, CNN)作為特徵擷取的方法，最後使用K-近鄰演算法(K Nearest Neighbor, KNN) 作為辨識比對的方法，並將此系統架構建立在行動裝置上使用。

Biological features of the human can be divided into physiological features and behavioral features, physiological features such as face, fingerprint, vein, palmprint, bone, iris, etc., belong to the static features, behavioral features such as signature, voiceprint, heartbeat, gait, etc., belong to the dynamic features, these biological features has individual uniqueness and stability, therefore, we can use these features as determining personal identity, develop each type of biometric technology. Meanwhile, with information technology and internet development, mobile device has been very popular, and the range of applications is extensive, personal information security has become more important, so that the biometric technology combined with the mobile device in the security verification attention on.
In these biometric techniques, face recognition features are natural and non-contact. Naturalness refers to a human individual identification by using biological characteristics. For example, face recognition is comparing faces between different individuals to distinguish and confirm the identity. In addition, using faces as a biometric identification is easy and without contact, so it becomes the mainstream of biometric methods.
We propose a face recognition system based on mobile device using haar-like feature to detect human face. And preprocessing the image with the scale normalization, rotation normalization and illumination normalization. And use Convolutional Neural Network as the method to extract feature. Then use K Nearest Neighbor as a method of recognition.

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