Deepgender: real-time gender classification using deep learning for smartphones

Haider, Khurram Zeeshan; Malik, Kaleem Razzaq; Khalid, Shehzad; Nawaz, Tabassam; Jabbar, Sohail

doi:10.1007/s11554-017-0714-3

Cited by 37 publications

(31 citation statements)

References 22 publications

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“…Application Scenarios. Figure 1 shows typical applications of computational-resource-limited DL in the smart sensing context, including self-driving [25,26], artificial intelligence APPs of smartphones [27], health/homecare robots [28][29][30][31], and intelligent wearable devices [32]. e DNN can be pretrained on remote cloud while the mobile DL platforms communicate with the cloud and perform inference based on local computational and energy resources [33].…”

Section: Computational-resource-limited Context Of Deep Learningmentioning

confidence: 99%

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Chen

Zhang

et al. 2020

Mobile Information Systems

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Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.

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Section: Computational-resource-limited Context Of Deep Learningmentioning

confidence: 99%

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Chen

Zhang

et al. 2020

Mobile Information Systems

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“…Only in a few cases the analysis is carried out on more than one benchmark (Azzopardi et al 2018b) (Foggia et al 2019) (Afifi and Abdelhamed 2019) to evaluate the generalization capabilities of the methods and, in most of the papers, the experimentation is performed on images not representing the real conditions. Think, as an example, to the method proposed by Haider et al (2019), namely a CNN optimized for smartphones; despite the necessity of dealing with the challenging faces captured with the camera of a smartphone, they evaluate the performance on datasets recorded in controlled laboratory conditions (CAS-PEAL-R1 and FEI), achieving an average accuracy of 0.95 that is not easily reproducible in the operating phase. The main considerations arising from the experimental analyses available in the literature are the following: (i) the conditions causing the performance degradation of the existing neural networks for gender recognition have not yet been deeply investigated; (ii) the methods are not evaluated in the real operating conditions.…”

Section: Datasetmentioning

confidence: 99%

Gender recognition in the wild: a robustness evaluation over corrupted images

Greco

Vento

Vigilante³

2020

J Ambient Intell Human Comput

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In the era of deep learning, the methods for gender recognition from face images achieve remarkable performance over most of the standard datasets. However, the common experimental analyses do not take into account that the face images given as input to the neural networks are often affected by strong corruptions not always represented in standard datasets. In this paper, we propose an experimental framework for gender recognition “in the wild”. We produce a corrupted version of the popular LFW+ and GENDER-FERET datasets, that we call LFW+C and GENDER-FERET-C, and evaluate the accuracy of nine different network architectures in presence of specific, suitably designed, corruptions; in addition, we perform an experiment on the MIVIA-Gender dataset, recorded in real environments, to analyze the effects of mixed image corruptions happening in the wild. The experimental analysis demonstrates that the robustness of the considered methods can be further improved, since all of them are affected by a performance drop on images collected in the wild or manually corrupted. Starting from the experimental results, we are able to provide useful insights for choosing the best currently available architecture in specific real conditions. The proposed experimental framework, whose code is publicly available, is general enough to be applicable also on different datasets; thus, it can act as a forerunner for future investigations.

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“…They use ML techniques to form a common embedding space for textual as well as visual data and accomplish image to sentence in that intermediate space to find the most appropriate captions for a query image. The third technique is to use datadriven approach, by this all image can be treated for captions as a caption transfer problem [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Data Augmentation to Stabilize Image Caption Generation Models in Deep Learning

Aldabbas¹,

Asad²,

Hashem³

et al. 2019

IJACSA

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Automatic image caption generation is a challenging AI problem since it requires utilization of several techniques from different computer science domains such as computer vision and natural language processing. Deep learning techniques have demonstrated outstanding results in many different applications. However, data augmentation in deep learning, which replicates the amount and the variety of training data available for learning models without the burden of collecting new data, is a promising field in machine learning. Generating textual description for a given image is a challenging task for computers. Nowadays, deep learning performs a significant role in the manipulation of visual data with the help of Convolutional Neural Networks (CNN). In this study, CNNs are employed to train prediction models which will help in automatic image caption generation. The proposed method utilizes the concept of data augmentation to overcome the fuzziness of well-known image caption generation models. Flickr8k dataset is used in the experimental work of this study and the BLEU score is applied to evaluate the reliability of the proposed method. The results clearly show the stability of the outcomes generated through the proposed method when compared to others.

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Deepgender: real-time gender classification using deep learning for smartphones

Cited by 37 publications

References 22 publications

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Gender recognition in the wild: a robustness evaluation over corrupted images

Data Augmentation to Stabilize Image Caption Generation Models in Deep Learning

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