Robust deep learning features for face recognition under mismatched conditions

In this paper, we addressed the problem of face recognition under mismatched conditions. In the proposed system, for face representation, we leveraged the state-of-the-art deep learning models trained on the VGGFace2 dataset. More specifically, we used pretrained convolutional neural network models to extract 2048 dimensional feature vectors from face images of International Challenge on Biometric Recognition in the Wild dataset, shortly, ICB-RW 2016. In this challenge, the gallery images were collected under controlled, indoor studio settings, whereas probe images were acquired from outdoor surveillance cameras. For classification, we trained a nearest neighbor classifier using correlation as the distance metric. Experiments on the ICB-RW 2016 dataset have shown that the employed deep learning models that were trained on the VGGFace2 dataset provides superior performance. Even using a single model, compared to the ICB-RW 2016 winner system, around 15% absolute increase in Rank-1 correct classification rate has been achieved. Combining individual models at feature level has improved the performance further. The ensemble of four models achieved 91.8% Rank-1, 98.0% Rank-5 identification rate, and 0.997 Area Under the Curve of Cumulative Match Score on the probe set. The proposed method significantly outperforms the Rank-1, Rank-5 identification rates, and Area Under the Curve of Cumulative Match Score of the best approach at the ICB-RW 2016 challenge, which were 69.8%, 85.3%, and 0.954, respectively.