不同於大多數的RGB-D人臉辨識，應用RGB-D影像於註冊以及辨識階段。本研究考慮一種情況，只用一組RGB-D的正面影像為註冊影像，而測試影像為具有大角度變化的2D RGB圖像。並且關切了在過去沒有注意到的一個問題，當註冊距離較遠時產生的量測誤差，對此提出一個利用高解析的深度影像為主成份基底，來重建充滿雜訊的人臉深度的方法。在多角度人臉方面利用自動的地標點定位使該重建之三維人臉可生成與辨識圖像做為匹配的特定二維圖像。為了要處理包含眼鏡的人臉樣本，創建了三維眼鏡加入三維人臉模型中，以提升含眼鏡之人臉樣本的辨識率。並且在公開資料庫Biwi Kinect Head Pose Database、Curtin face以及Eurecom Database上測試，顯示出加了深度圖像後能夠大幅度的增加跨角度的辨識性能，並自行蒐集了RGBDFaces database比較不同距離下所拍攝之Kinect 深度資訊以研究量測誤差對人臉辨識的影響。
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Different from most RGB-D face recognition with RGB-D images available in both registration and recognition phases, this study considers RGB-D images only at the registration phase and the recognition is performed on RGB images. The quantization noise, often encountered when the subject is not close enough to the camera at registration, has not attracted much attention in the past, but is discussed and resolved by a proposed approach. The proposed approach exploits the principal components extracted from high resolution depth data as the basis for the reconstruction of the noise-corrupted facial depth. To deal with faces with eyeglasses, a reference facial model with eyeglasses is considered in the reconstruction. The performance of the proposed approach is evaluated on three public databases and the RGBDFaces that we made for studying the impacts of quantization noises.

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