Gender and Smile Classification Using Deep Convolutional Neural Networks

Zhang, Kaipeng; Tan, Lianzhi; Li, Zhifeng; Qiao, Yu

doi:10.1109/cvprw.2016.97

Cited by 56 publications

(30 citation statements)

References 10 publications

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“…Recently, Liu et al [31] released CelebA dataset containing about 200, 000 near-frontal images with 40 attributes including gender and smile, which accelerated the research in this field [46], [36], [55], [10]. Faces of the world [13] challenge dataset further advanced the research on these tasks for faces with varying scale, illumination and pose [28], [44], [53]. Age Estimation is the task of finding the real or apparent age of a person based on their face image.…”

Section: Related Workmentioning

confidence: 99%

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

Ranjan

Sankaranarayanan

Castillo

et al. 2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

353

280

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We present a multi-purpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single deep convolutional neural network (CNN). The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks. Extensive experiments show that the network has a better understanding of face and achieves state-of-the-art result for most of these tasks.

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Section: Related Workmentioning

confidence: 99%

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

Ranjan

Sankaranarayanan

Castillo

et al. 2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

353

280

View full text Add to dashboard Cite

show abstract

“…The latter approaches are more robust and have provided state-of-the-art performances recently. Furthermore, global approaches have been implemented as multi-tasking approaches [3] and in some cases employing specific models to classify each attribute [12], [20]. More recently, multi-task models have explored the correlation between attributes to improve classification performances such as [8].…”

Section: Related Workmentioning

confidence: 99%

“…However, it can be effective when considering specific attributes like smile and gender. Zhang et al [20] learn to classify gender and smile attributes from facial images using two separate networks (GNet and SNet). An exciting part of the study is the use of the correlation between specific attributes to improve performance in low data regimes.…”

Section: Related Workmentioning

confidence: 99%

Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset

Ali-Gombe

Elyan

Jayne

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm or the data itself is required before attempting a classification task. Data augmentation is one common approach used to improve the presence of the minority class instances and rebalance the dataset. However, simple augmentation techniques such as applying some affine transformation to the data, may not be sufficient in extreme cases, and often do not capture the variance present in the dataset. In this paper, we propose a new approach to generate more samples from minority class instances based on Generative Adversarial Neural Networks (GAN). We introduce a new Multiple Fake Class Generative Adversarial Networks (MFC-GAN) and generate additional samples to rebalance the dataset. We show that by introducing multiple fake class and oversampling, the model can generate the required minority samples. We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class. Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples.

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“…The results also show the effectiveness of our proposed architecture in comparison with previous state-of-the-art methods. [46] 87.30% 84.90% 86.10% IVA_NLPR [47] 82.52% 91.52% 87.02% SIAT_MMLAB [48] 89…”

Section: Comparison With Previous Approachesmentioning

confidence: 99%

Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

Fan

Kim

et al. 2019

Applied Sciences

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Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

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Gender and Smile Classification Using Deep Convolutional Neural Networks

Cited by 56 publications

References 10 publications

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

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

Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset

Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

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