One-shot deep neural network for pose and illumination normalization face recognition

Wu, Zhongjun; Deng, Weihong

doi:10.1109/icme.2016.7552902

Cited by 22 publications

(8 citation statements)

References 10 publications

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“…Likewise, [114] synthesised frontal face using 3D Generic Elastic Model (3DDEM) with texture mapping. [115] generate a frontal face from five facial landmark 3D mesh in a single reference. Deep learning models also explore 2D and 3D model fitting for pose normalisation.…”

Section: ) Normalizationmentioning

confidence: 99%

“…The deep learning model was able to synthesis frontal faces from the training of several multi-posed data. [115] used the deep learning method to achieve pose and illumination normalisation, and they trained a deep neural network with face images generated from 3DGEM. [116] introduced the Face Frontalization Generative Adversarial model (FF-GAM) using 3DMM.…”

Section: ) Normalizationmentioning

confidence: 99%

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Facial Expression Recognition: A Review of Trends and Techniques

Ekundayo

Viriri

2021

IEEE Access

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Facial Expression Recognition (FER) is presently the aspect of cognitive and affective computing with the most attention and popularity, aided by its vast application areas. Several studies have been conducted on FER, and many review works are also available. The existing FER review works only give an account of FER models capable of predicting the basic expressions. None of the works considers intensity estimation of an emotion; neither do they include studies that address data annotation inconsistencies and correlation among labels in their works. This work first introduces some identified FER application areas and provides a discussion on recognised FER challenges. We proceed to provide a comprehensive FER review in three different machine learning problem definitions: Single Label Learning (SLL)-which presents FER as a multiclass problem, Multilabel Learning (MLL)-that resolves the ambiguity nature of FER, and Label Distribution Learning-that recovers the distribution of emotion in FER data annotation. We also include studies on expression intensity estimation from the face. Furthermore, popularly employed FER models are thoroughly and carefully discussed in handcrafted, conventional machine learning and deep learning models. We finally itemise some recognise unresolved issues and also suggest future research areas in the field.

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Section: ) Normalizationmentioning

confidence: 99%

Section: ) Normalizationmentioning

confidence: 99%

Facial Expression Recognition: A Review of Trends and Techniques

Ekundayo

Viriri

2021

IEEE Access

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“…For each group of neurons, a mini batch of images corresponding to changes in only a single scene variable is used for training. Wu et al [13] convert images into recon code representing poses and illumination conditions and then reconstruct images in the frontal view and the neutral lighting condition. With the aid of 3D face models, enormous training samples are generated to optimize their network.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], the local pattern extraction layer and the illumination elimination layer are designed and integrated into a Convolutional Neural Network (CNN) to obtain illumination invariant feature maps. Wu et al [13] devise a multi-task DNN in order to complete the tasks of normalization and reconstruction. Generative Adversarial Network (GAN) with four types of loss function is utilized in [14] to generate images under several fixed illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Illumination Normalization for Face Recognition Based on Auto-Encoder

Wang

2020

IEEE Access

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Nonuniform illumination is one of the main issues that hinder the accuracy of face recognition because it makes the intra-person variation more complicated. To minimize the intra-person differences caused by varying illuminations, this paper presents a normalization method based on Convolutional Auto-encoder (CAE). The CAE is employed to map the face images under various illumination conditions to a normalized one, generating preliminary results with blurry and insufficient facial details, which are tricky for recognition. To recover these details, a restoration method based on re-blurring strategy and frequency analysis is proposed to preserve the facial features lying in high-frequency components based on discrete cosine transform (DCT). Therefore, in our method, these components are extracted and reintroduced into the outputs of CAE to enhance the fidelity of outputs. Thus, the facial details are preserved to the largest degree and the following works such as recognition tasks are benefited. Experiments conducted on the AR, extended Yale B, and CAS-PEAL database demonstrate the effectiveness of our method.

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“…Multimodal data has been studied for a variety of applications to analyze human behaviors, including person detection and identification [9,10], human action recognition [11,12], face recognition [13,14], as well as sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Select-additive learning: Improving generalization in multimodal sentiment analysis

Wang

Meghawat

Morency

et al. 2017

2017 IEEE International Conference on Multimedia and Expo (ICME)

170

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Multimodal sentiment analysis is drawing an increasing amount of attention these days. It enables mining of opinions in video reviews which are now available aplenty on online platforms. However, multimodal sentiment analysis has only a few high-quality data sets annotated for training machine learning algorithms. These limited resources restrict the generalizability of models, where, for example, the unique characteristics of a few speakers (e.g., wearing glasses) may become a confounding factor for the sentiment classification task. In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis. In our experiments, we show that our SAL approach improves prediction accuracy significantly in all three modalities (verbal, acoustic, visual), as well as in their fusion. Our results show that SAL, even when trained on one dataset, achieves good generalization across two new test datasets.

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One-shot deep neural network for pose and illumination normalization face recognition

Cited by 22 publications

References 10 publications

Facial Expression Recognition: A Review of Trends and Techniques

Facial Expression Recognition: A Review of Trends and Techniques

High-Fidelity Illumination Normalization for Face Recognition Based on Auto-Encoder

Select-additive learning: Improving generalization in multimodal sentiment analysis

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