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Multi-Angle Face Recognition based on Data Augmentation and It's Application to Realtime Image Search
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部照片當作線索，希望透過一萬台監視器即時取得該秒一萬個影像辨識嫌疑犯並找出嫌疑人所在位置。文中將透過MTCNN  擷取畫面中臉部特徵，並使用FaceNet  識別嫌疑人。在缺乏嫌犯臉部照片情況下，人臉識別方法只能透過兩張照片轉成特徵向量計算相似度距離找出最相似的照片。在200 張的實驗下能有87% 的準確度判斷出嫌疑人。在2000 張的實驗下，準確度剩下79.2%。當照片提升至10000 張時，準確度則下降到45.1%。文中藉由PRNet  實現2D 照片3D臉部建模，透過3D 模型增量產出各角度臉部照片，並訓練分類器，將臉部辨識準確度由45.1% 提升至71.4%。|
In the study, the situation of the police investigation case will be simulated. Suppose there is a suspect with only one face photo as a clue. I hope to get 10,000 images to identify the suspect and find the suspect through 10,000 monitors. The face features in the picture will be captured through MTCNN  and the suspect will be identified using FaceNet . In the absence of suspected face photos, the face recognition method can only find the most similar photos by calculating the similarity distance by converting two photos into feature vectors. The suspect can be judged with an accuracy of 87% in 200 experiments. Under the 2000 experiment, the accuracy is 79.2%. When the photo is increased to 10,000, the accuracy drops to 45.1%. In this paper, 2D photo 3D face modeling is implemented by PRNet , and the face images of each angle are incrementally generated through the 3D model, and the classifier is trained to increase the face recognition accuracy from 45.1% to 71.4%.
|Appears in Collections:||資訊科學與工程學系所|
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