A fast and robust 3D face recognition approach based on deeply learned face representation

Cai, Ying; Lei, Yinjie; Yang, Menglong; Zhou, You; Shan, Shiguang

doi:10.1016/j.neucom.2019.07.047

Cited by 51 publications

(25 citation statements)

References 48 publications

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“…Our method spends less time in both processing and matching. The proposed method only extracts the feature based on one branch of the Siamese network from end to end in the testing phase, so the proposed method has an edge over the method in literature [9], which contains not only a network but also a manually designed feature augmentation. Moreover, the matching time is decreased by 2-45 times by replacing the classic Euclidean distance measurement with a simple two-layer fully connected network.…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

“…However, the network scale increased with the people recognized, which made it hard to recognize large numbers of people. Cai [9] proposed a fast and robust face recognition method utilizing facial landmarks transferred to a deep neural network. The CNN coupled with transfer learning had the potential to overcome the expression varieties and over-fitting problems to improve the robustness of the methods.…”

Section: Introductionmentioning

confidence: 99%

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3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Sun

et al. 2021

Electronics

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Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

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Section: Methods Accuracy (%)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Sun

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

“…These rigid features can help face recognition systems overcome the inherent defects and drawbacks of 2D face recognition, for example, the facial expression, occlusion, and pose variations. Furthermore, a 3D model is relatively unchanged in terms of scaling, rotation, and illumination [5]. Most 3D scanners can acquire both 3D meshes/point clouds and corresponding textures.…”

Section: Introductionmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.

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“…Biometric recognition techniques utilise the difference in body features for recognition. Image recognition is one of the more common biometric techniques used in many applications [20][21][22][23][24]. This method uses human image data to distinguish one person from another.…”

Section: Introductionmentioning

confidence: 99%

Adaptive gender-based thermal control system

Al-Nabulsi

Badr

2021

IJECE

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A closed loop adaptive gender-based thermal control system (AG-TCS) is designed, modelled, analysed and tested. The system has the unique feature of adapting to the surrounding environment as a function of the number of humans present and the gender ratio. The operation of the system depends on a unique interface between a radio frequency identification (RFID) device and an imaging device, both of which are correlated and interfaced to a controller. Testing of the system resulted in smooth transition and shape conversion of the response curve, which proved its adaptability. Three mathematical equations describing the internal mechanisms of the AG-TCS are presented and have been proven to optimally reflect the original statistical data covering both genders.

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A fast and robust 3D face recognition approach based on deeply learned face representation

Cited by 51 publications

References 48 publications

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

3D Face Recognition: A Survey

Adaptive gender-based thermal control system

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