scholarly journals Effective training of convolutional neural networks for age estimation based on knowledge distillation

2021 ◽  
Author(s):  
Antonio Greco ◽  
Alessia Saggese ◽  
Mario Vento ◽  
Vincenzo Vigilante
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Resource Constraints ◽  
Training Procedure ◽  
Face Images ◽  
Effective Training ◽  
Knowledge Distillation ◽  
Student Models ◽  
Teacher Model

AbstractAge estimation from face images can be profitably employed in several applications, ranging from digital signage to social robotics, from business intelligence to access control. Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face analysis tasks. However, these networks are not always applicable in real scenarios, due to both time and resource constraints that the most accurate approaches often do not meet. Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks. Within this context, we propose in this paper an effective training procedure of CNNs for age estimation based on knowledge distillation, able to allow smaller and simpler “student” models to be trained to match the predictions of a larger “teacher” model. We experimentally show that such student models are able to almost reach the performance of the teacher, obtaining high accuracy over the LFW+, LAP 2016 and Adience datasets, but being up to 15 times faster. Furthermore, we evaluate the performance of the student models in the presence of image corruptions, and we demonstrate that some of them are even more resilient to these corruptions than the teacher model.

scholarly journals Age estimation based on face images and pre-trained convolutional neural networks

2017 ◽  
Author(s):  
Abhinav Anand ◽  
Ruggero Donida Labati ◽  
Angelo Genovese ◽  
Enrique Munoz ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Face Images

scholarly journals Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 146 ◽  
Author(s):  
Xinhua Liu ◽  
Yao Zou ◽  
Hailan Kuang ◽  
Xiaolin Ma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Data Augmentation ◽  
Rapid Development ◽  
Estimation Methods ◽  
Face Images

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.

scholarly journals Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

2021 ◽  
Vol 5 (4) ◽  
pp. 1-28
Author(s):  
Chia-Heng Tu ◽  
Qihui Sun ◽  
Hsiao-Hsuan Chang
Keyword(s):  
Neural Networks ◽  
Environmental Monitoring ◽  
Convolutional Neural Networks ◽  
Model Building ◽  
Software Framework ◽  
Training Procedure ◽  
Monitoring Methods ◽  
Resource Constrained ◽  
Resource Constrained Devices ◽  
Constrained Devices

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

A Data Augmentation Methodology to Improve Age Estimation Using Convolutional Neural Networks

2016 ◽  
Author(s):  
Italo de Pontes Oliveira ◽  
Joao Lucas Peixoto Medeiros ◽  
Vinicius Fernandes de Sousa ◽  
Adalberto Gomes Teixeira Junior ◽  
Eanes Torres Pereira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Data Augmentation

scholarly journals Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

2018 ◽  
Vol 8 (9) ◽  
pp. 1601
Author(s):  
Chaoqun Hong ◽  
Zhiqiang Zeng ◽  
Xiaodong Wang ◽  
Weiwei Zhuang
Keyword(s):  
Neural Networks ◽  
Age Estimation ◽  
Feature Fusion ◽  
Estimation Method ◽  
Feature Space ◽  
Image Features ◽  
Low Rank ◽  
Face Images ◽  
Low Rank Representation ◽  
Multiple Network

Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.

scholarly journals Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas

2020 ◽  
Author(s):  
◽  
L. F. Buzuti
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pain Assessment ◽  
Neural Models ◽  
External Variable ◽  
Neonatal Pain ◽  
Face Images ◽  
Average Accuracy ◽  
Late Intervention ◽  
Quantitative Results

Neonatal pain assessment might suffer variation among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to develop computational tools of pain assessment, less subjective and susceptible to external variable influences. Deep learning models, especially Convolutional Neural Networks, have gained ground in the last decade, due to many successful applications in image analysis, object recognitions and human emotion recognitions. In this context, the general aim this dissertation was analyse quantitatively and qualitatively models of Convolutional Neural Networks in the task neonatal pain classification through a computacional framework based in face images of two distinct databases (an international, named COPE, and other national, named UNIFESP). How specific aims were implemented, evaluated and compared the performance of three existent models used in literature: Neonatal Convolutional Neural Network (N-CNN) and two type of ResNet50 models. The quantitative results showed the excellence of N-CNN to neonatal pain assessment automatic, with average accuracy of 87.2% and 78.7% for the databases COPE and UNIFESP, respectively. However, the quantitative analysis showed that all neural models evaluated, including N-CNN models, can learn artifacts from the imagens and not variation discriminating in faces, thus showed the necessity more studies to apply this models in clinical practice

Effective training of convolutional neural networks for face-based gender and age prediction

2017 ◽  
Vol 72 ◽  
pp. 15-26 ◽  
Author(s):  
Grigory Antipov ◽  
Moez Baccouche ◽  
Sid-Ahmed Berrani ◽  
Jean-Luc Dugelay
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gender And Age ◽  
Effective Training ◽  
Age Prediction
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