Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation

Smith, Philip; Chen, Cuixian

doi:10.1109/bigdata.2018.8621891

Cited by 50 publications

(22 citation statements)

References 24 publications

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“…The Visual Geometry Group Network (VGG) was published in ICLR 2015 and received proxime accessit in the ImageNet Challenge 2014 [26]. Due to its good structural adaptability, VGG is widely used in the fields of image feature extraction and transfer learning [27]. The structure of VGG-16 layer is shown in Fig.…”

Section: The Modified Visual Geometry Group Network 1) Visual Geommentioning

confidence: 99%

A Novel MI-EEG Imaging With the Location Information of Electrodes

Han

Duan

2020

IEEE Access

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Combination of the Motor Imagery EEG (MI-EEG) imaging and Deep Convolutional Neural Network is a prospective recognition method in brain computer interface. Nowadays, the frequency or timefrequency analysis has been applied to each channel of MI-EEG signal to obtain a spatio-frequency or timefrequency image, and even the images from several channels are infused to generate a combined image. However, the real position information of channels or electrodes is lost in these MI-EEG images, and this is contradictory to the activation area of MI-tasks. In this paper, the MI period and the frequency band covered by µ and β rhythms are divided into ten time windows and three sub-bands, respectively. Then, for each electrode, Fast Fourier Transform (FFT) is employed to transform each time window to spectrum, and its inverse FFT is calculated for each sub-band. The time-domain powers of ten time windows are averaged for the same sub-band. So, three average powers are generated as the time-frequency features of each electrode of MI-EEG. They are further arranged to the electrode coordinate figure by using Clough-Tocher interpolation algorithm, and a complicated image, in which the time-frequency features are correctly located at the real position of each electrode, is obtained to embody the MI-EEG in detail. Furthermore, a VGG network is modified to perform effective recognition for MI-EEG image, and it is called mVGG. Extensive experiments are conducted on three publicly available datasets, and the 10-folds cross validation accuracies of 88.62%, 92.28% and 96.86% are achieved respectively, and they are higher than that of the state-of-the-art imaging methods. Kappa values and ROC curves demonstrate our method has lower class skew and error costs. The experimental results show that the effectiveness of proposed MI-EEG imaging method, and it is well-matched with mVGG. INDEX TERMS Brain computer interface, convolutional neural network, interpolation method, machine learning, MI-EEG imaging method.

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Section: The Modified Visual Geometry Group Network 1) Visual Geommentioning

confidence: 99%

A Novel MI-EEG Imaging With the Location Information of Electrodes

Han

Duan

2020

IEEE Access

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“…These two papers are the main academic resources whose concept we will utilize into the model to achieve the desirable results. Smith and Chen [8] proposed a method using transfer learning with deep CNNs. The transfer learning is based on VGG19 and VGGFaces with deep CNN hierarchy.…”

Section: Related Workmentioning

confidence: 99%

Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet

Huynh

Nguyễn

2020

SN COMPUT. SCI.

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Two key facial features, age and gender, have been widely explored. Companies and organizations have investigated in related applications in several fields including insurance, retails, marketing, etc. It would bring tremendous benefit, which allow companies to easily identify their customer demographics. Several approaches have been proposed with remarkable results. However, because of the lack of open and multi-ethnic datasets, most modern age and gender estimating models were trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. In this paper, we developed an applicable Wide ResNet model to estimate the age and the gender of Asian faces. The model was trained with a newly improved Asian face database. The experiments have shown promising results, as it can match the performance of Microsoft's how-old API estimator in a specific dataset. Keywords Deep learning • Convolutional neural network • ResNet • Wide ResNet • Age and gender estimation This article is part of the topical collection "Future Data and Security Engineering 2019" guest edited by Tran Khanh Dang.

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“…The fine-tuning is achieved using transfer learning techniques. 53 The models were trained using the PyTorch framework. 54 Binary cross-entropy is used as the loss function during training along with a stochastic gradient descent (SGD) 55 optimizer.…”

Section: Initial Training and Transfer Learning Of Pretrained Networkmentioning

confidence: 99%