近年來人們科技正在進步，使得人類的生活越來越便利，把資料存放置電腦甚至雲端已經是越來越普及的行為，因此對於保護資料的安全成為非常重要的課題，許多系統看中身份認證所具備的身份特性來進行鑒定，其身份特性包含了獨特性、方便性、可靠性與不容易偽造…等， 本次研究將重心放在3D手部資料之身份辨識，手部資料包括指紋、掌紋、指節紋路、手部整體形狀，使用的資料本身是以點雲的型式儲存，利用投影的方式結合3D分類器與2D分類器兩者之優點來實現身份辨識。
考慮到了3D數據的複雜性，為了讓模型能同時具有3D分類器的高準確度與2D分類器的低系統複雜度之優點，我們利用投影的方式將3D資料轉成2D資料型式，將經過投影處理的資料傳入特別設計分類器模型之中，此作法包含了許多的好處，包含了能使用2D的資料增強方法(Mixup、Crop…)與2D 分類器所包含的多樣性，通過實驗顯示利用所提出的投影方式加上所設計出MobileNet-LPB模型相比於3D-PointNet與2D-MoblieNetv2有顯著的性能差異。

As the progress of technology, as well as human life, has become more convenient. It is a prevalent behavior that is uploading personal data to the cloud. So, how to protect them has become an important issue, and human recognition systems play an essential role there. Many methods focus on individual characteristics to identify. These characteristics include uniqueness, convenience, reliability, and not ease to forge. In this paper, we focus on how to classify the 3D-palms data. The data consist of the fingerprint, palm print, knuckle pattern, and overall shape of the hand. Considering the complexity of 3D data, we project the 3D data to 2D for many benefits of 2D classification. Such as various 2D augmentation methods - MixUP or random crop......, diversity of classifier - VGG, ResNet, and others. Finally, the proposed MobileNet-LPB with the proposed projection method has a significant performance gap over the 2D-MoblieNetv2 and 3D-PointNet in the benchmark.

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