在眾多神經網路的相關應用中，臉部辨識與年齡辨識是相關的應用之一，能做到其中一種辨識的神經網路比比皆是，若是有同時需要兩種結果的情境，也只要同時各自使用臉部辨識與年齡辨識的神經網路就能滿足需求。然而在要求即時性且運算資源較為吃緊的行動裝置上，同時應用多個神經網路的方式可能會因為運算時間的增加而失去了即時性，若能將一神經網路的計算結果運於其他的神經網路中，便能降低另一網路的複雜度與計算時間。故本研究針對臉部辨識與年齡辨識神經網路提出整合的方法，使應用程式僅需要使用一個臉部辨識神經網路作為臉部辨識使用，並將其輸出用於年齡辨識神經網路，藉此大幅減少年齡辨識網路所需的空間與時間。
本研究最終的年齡辨識結果略遜於現有的年齡辨識神經網路，但從實驗結果可以認為臉部辨識神經網路的輸出結果中確實包含著輸入影像的年齡資訊，使用此方式進行年齡辨識是可行的。

Although there are many neural networks related to facial recognition and age prediction, few can be applied to mobile devices, and there are few applications that have both facial recognition and age prediction functions. The goal of this research is to train a neural network for face and age recognition on mobile devices and use it in a smartphone application. The input of the age recognition neural network is the features extracted by the face recognition neural network, thereby reducing the complexity and computing time of the age recognition network.
The current age recognition results in this study are slightly inferior to other age recognition neural networks, but according to the experimental results, it can be considered that the output of the face recognition neural network does indeed contain the age information of the input image, and the use of this output for age recognition is feasible.

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