Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership

Deng, Weihong; Hu, Jiani; Zhang, Nanhai; Chen, Binghui; Guo, Jun

doi:10.1016/j.patcog.2016.11.023

Cited by 72 publications

(44 citation statements)

References 34 publications

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“…The results of some benchmark methods are shown in the top half of the table. These results are publicly accessible 4 and provided by the SLLFW team [26]. As can be seen from Table 3, Gico loss achieves considerably better performance than other methods on SLLFW.…”

Section: Results On Lfw Ytf and Sllfwmentioning

confidence: 97%

Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Wei

Wang

Scotney

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Loss function plays an important role in CNNs. However, the recent loss functions either do not apply weight and feature normalisation or do not explicitly follow the two targets of improving discriminative ability: minimising intra-class variance and maximising inter-class variance. Besides, all of them consider only the feedback information from the current mini-batch instead of the distribution information from the whole training set. In this paper, we propose a novel loss function -Global Information-based Cosine Optimal loss (Gico loss). Gico loss is applied with weight and feature normalisation, designed explicitly following the aforementioned two targets of improving discriminative ability, and is guided by the distribution information from the whole training set. Extensive experiments are conducted on multiple public datasets, which confirms the effectiveness of the proposed Gico loss and shows that we achieve state-of-the-art performance.

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Section: Results On Lfw Ytf and Sllfwmentioning

confidence: 97%

Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Wei

Wang

Scotney

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…To determine the pairs of subjects who look more alike, we have used the Similar-looking LFW database (SLLFW) [70], which offers 3000 pairs of similar-looking faces (using the images of LFW). We have picked 25 pairs of images from it considering two factors.…”

Section: A Selection Of the Facial Identification Algorithmmentioning

confidence: 99%

Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

Carabe

Cermeño²

2021

IEEE Access

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Face identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject's image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects' images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneously.

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“…We also verify the performance of our Improved softmax loss on the similar-looking (SLLFW [25]), cross-age (CALFW [26]), and cross-pose (CPLFW [27]) datasets. The three datasets were set up as much more challenging options to LFW.…”

Section: Datasetmentioning

confidence: 99%

Improved softmax loss for deep learning‐based face and expression recognition

Zhou

Jia

Shen

et al. 2019

Cognitive Computation and Systems

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In recent years, deep convolutional neural networks (CNN) have been widely used in computer vision and significantly improved the performance of image recognition tasks. Most works use softmax loss to supervise the training of CNN and then adopt the output of last layer as features. However, the discriminative capability of the softmax loss is limited. Here, the authors analyse and improve the softmax loss by manipulating the cosine value and input feature length. As the approach does not change the principle of the softmax loss, the network can easily be optimised by typical stochastic gradient descent. The MNIST handwritten digits dataset is employed to visualise the features learned by the improved softmax loss. The CASIA-WebFace and FER2013 training set are adopted to train deep CNN for face and expression recognition, respectively. Results on both the LFW dataset and FER2013 test set show that the proposed softmax loss can learn more discriminative features and achieve better performance.

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Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership

Cited by 72 publications

References 34 publications

Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

Improved softmax loss for deep learning‐based face and expression recognition

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