因此，本研究嘗試透過社群媒體上蒐集黑名單白名單人臉照片，利用深度學習中的卷積神經網路（CNN）演算法進行寵物飼主的好壞辨識。本文的人臉識別CNN 模型結構，由六個卷積層和四個池化層，以及全連接層中的兩個隱藏層組成，我們使用了 Data augmentation（數據增強）來解決數據集不足的問題，使用Batch Normalization（批次正規化）克服模型難以訓練的問題，使用 Dropout 等方法減緩過擬合，並且使用 Adam 優化器和 Softmax 分類器進行人臉識別可以使訓練更穩定、更快收斂，有效提高準確率。
透過卷積神經網路很強的特徵提取圖像辨識的方式，可以快速有效地對寵物飼養人進行辨別。實驗結果表明，本研究 CNN 模型在寵物飼主人臉照片上的識別率為 80.09%。;Along with the change in population structure and the social-economic pattern, adopting pets have become popular. However, as raising pets requires long term devotion and commitment, some pet owners may change their minds or may resort to abusing pets to release their emotion. To preventing pets from becoming the victims of bad owners, how to deter the potential abusers to adopt pets has become an important issue. As people with unstable emotion tend to have certain types of facial expression, this study proposed to build up discriminant model for bad owners by analyzing pictures. Even though analyzing facial images with Nero networks have been studied in many areas, to the best of our knowledge, no one has applied related knowledge to this issue.
The proposed model utilizes CNN with six Convolution layers, four Pooling layers, and two Fully Connected Layer at the end. As the number of pictures was not sufficient, Data augmentation was utilized to increase the data size. Batch Normalization is utilized to quickly converge the model parameters as the number of data are still relative limited. Dropout and regularization methods are also adopted to relieved the issue of overfitting. Numerous hyper parameters tuning were attempted, and result showed that the accuracy can reach 80.09%.
