Soft-Ranking Label Encoding for Robust Facial Age Estimation

Zeng, Xusheng; Huang, Junyang; Ding, Changxing

doi:10.1109/access.2020.3010815

Cited by 37 publications

(24 citation statements)

References 52 publications

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“…(a) A frame from the video "Walking Next to People" 11 . (b) The processed result by using different perceiving methods, i.e., semantic segmentation [85]; object detection [106]; text spotting [91]; human parsing [86]; human pose estimation [107]; face detection, alignment, and facial attribute analysis [96], [108]; depth estimation [109]. and camera-based solutions have been proposed by estimating the connections between smartphones and WI-FI access points or Bluetooth beacons [126] or estimating the crowd density of a crowd image [127].…”

Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

412

168

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In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

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Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

412

168

View full text Add to dashboard Cite

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“…Diaz et al [11] propose soft labels for ordinal classification (SORD) by considering the relationship between classes. Zeng et al [12] apply the soft label on ranking CNN to enhance the robustness. Generative Adversarial Network (GAN) is applied to solve some specific problems.…”

Section: B Age Recognitionmentioning

confidence: 99%

Soft Label With Channel Encoding for Dependent Facial Image Classification

Chiu

et al. 2022

IEEE Access

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In classification tasks, training labels are usually specified as one-hot targets which represent each class equally and exclusively. However, this labeling rule is not suitable in some situations. For the dependent classes, one-hot targets are not capable to represent the relation among them. The existing label smoothing methods just split the target response into neighboring classes, but it is only applied for ordinal classification, but not for the dependent but non-ordered classes. In this paper, we propose a novel labeling rule that decomposes the one-hot target into several bases to reflect relationships among classes while maintaining a balanced target space, which adopts channel encoding from communication systems, in particular, Bose-Chaudhuri-Hocquenghem (BCH) encoding. Besides, BCH encoder has an error-correcting mechanism that is expected to lift the accuracy. In theory, training with BCH targets ensures improved classification performance given the original accuracy is not less than 50%. To verify the proposed method on dependent classification, we conduct experiments with two facial tasks: age recognition and face antispoofing. The former is an ordinal classification task, and the latter is also regarded as a specific dependent classification problem due to the varying attack types being classified as one class finally and real for the other. Experimental results show that the proposed method improves accuracy by 6.33% on age recognition and reduces HTER by 3.63% for face anti-spoofing. In addition, as BCH targets divide the original response into a higher dimensional space, that is, the model is made to be heeded on learning the delicate subfeatures. Hence, BCH targets also enhance model generalizability, thus guaranteeing improved performance on cross-domain evaluations. We further perform an assessment on the PACS dataset for evaluating domain generalizability. The results show that the domain generalizability is enhanced by increasing average accuracy by over 2% training with BCH targets.

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“…Moreover, pose-invariant is also a challenge for facial analysis, and the details can be seen in work [4]. To solve this problem, recently, many state-of-the-art works are proposed, such as [26], [27], [28], [29], and [30].…”

Section: Related Work a Face Alignmentmentioning

confidence: 99%

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Feng

Wang

2021

IEEE Access

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Face expression recognition is a key technology of robot vision, which can help the robotic understand human emotions. However, interference from the real-world, such as light changes, face occlusion, and pose variation, reduces the recognition rate of the model. To solve above problems, in this paper, a novel deep model is proposed to improve the classification accuracy of facial expressions. The proposed model has the following merits: 1) A pose-guided face alignment method is proposed to reduce the intra-class difference, which can overcome the impact of environmental noise; 2) A hybrid feature representation method is proposed to obtain high-level discriminative facial features that achieves better results in classification networks; 3) A lightweight fusion backbone is designed, which combines the VGG-16 and the ResNet to achieve low-data and low-calculation training. Finally, to evaluate the proposed model, we conduct a series of experiments on four benchmark datasets, including the CK+, the JAFFE, the Oulu-CASIA, and the AR. The results show that the proposed model achieves state-of-the-art recognition rates, that is, 98.9%, 96.8%, 94.5%, and 98.7%, respectively. Comparing with the traditional methods and other advanced deep learning methods, the proposed model can comparable performance in a variety of tasks.

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Soft-Ranking Label Encoding for Robust Facial Age Estimation

Cited by 37 publications

References 52 publications

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Soft Label With Channel Encoding for Dependent Facial Image Classification

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

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