An All-In-One Convolutional Neural Network for Face Analysis

Ranjan, Rajeev; Sankaranarayanan, Swami; Castillo, Carlos D.; Chellappa, Rama

doi:10.1109/fg.2017.137

Cited by 330 publications

(269 citation statements)

References 57 publications

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“…Fine-tuning deep models to perform a task similar to the original one has been successfully used in other studies, where these networks have been used to detect other attributes related to face different than the identity, such as gender, age or race [15]. Since Quality and Accuracy are closely related, a feature vector that comprises the discriminative information of faces (Accuracy), is expected to also comprise the information of their Quality.…”

Section: Regression Model and Trainingmentioning

confidence: 99%

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

Hernandez-Ortega

Galbally²,

Fiérrez

et al. 2019

2019 International Conference on Biometrics (ICB)

106

112

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In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub 1 .

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Section: Regression Model and Trainingmentioning

confidence: 99%

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

Hernandez-Ortega

Galbally²,

Fiérrez

et al. 2019

2019 International Conference on Biometrics (ICB)

106

112

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“…For face identification, a major accomplishment of DCNNs is their visual robustness to changes in viewpoint, illumination, expression, and appearance (AbdAlmageed et al, 2016;Chen, Patel, & Chellappa, 2016;Ranjan, Sankaranarayanan, Castillo, & Chellappa, 2017;N. Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification

Parde

Castillo

et al. 2019

Cognitive Science

Self Cite

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Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training.

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“…Several studies have been conducted on this approach so far, which have gained considerable amount of improvement in accuracy [16], [15], [11]. However, we point out that there is still room for improvement in the current state-of-the-art.…”

Section: Introductionmentioning

confidence: 90%

“…We also conducted an ablation test with respect to the proposed combined loss. Specifically, we compare the performance of the proposed method with and without using the classification loss; the proposed loss combined without using the classification loss is identical to the standard regression loss (L2 loss) used in [16], [15], [11]. Thus, we train our CNN with and without using the classification loss for several K values on the 300W-LP dataset for 100 epochs, and then test it on the AFLW2000 dataset.…”

Section: E Comparison With a Standard Regression Lossmentioning

confidence: 99%

Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

Shao

Sun

Özay

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation. The Tensorflow implementation of our work is available at https://github.com/ MingzhenShao/HeadPose

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An All-In-One Convolutional Neural Network for Face Analysis

Cited by 330 publications

References 57 publications

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification

Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

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