Automatic gender recognition for “in the wild” facial images using convolutional neural networks

Nistor, Sergiu Cosmin; Marina, Alexandra-Cristina; Darabant, Adrian Sergiu; Borza, Diana

doi:10.1109/iccp.2017.8117018

Cited by 6 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This difference can be explained by the fact that only Gabor filters were used in [32] as hand-crafted features. Furthermore, the CNN accuracy achieved in this research is higher than that reported in [41], where a CNN model trained on the Adience dataset achieved 84% accuracy.…”

Section: Resultscontrasting

confidence: 73%

“…Numerous early studies have been criticized for benchmarking their works with constrained datasets, such as FERET [19][20][21][22] and UND [20] because they do not reflect real-world situations [23,39]. Therefore, many studies were aimed at the challenges posed by the images taken under uncontrolled conditions, for example, LFW [20,22] and Adience [17,23,32,40,41] datasets and datasets with occlusions (e.g., sunglasses and hats), such as AR [20,22], Gallagher [32], and MORPH [40]. The authors in [17] offered a unique unconstrained and unfiltered dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, to simulate real-life situations, recent studies [17,21,22,31] have adopted a cross-data approach, where one dataset is trained, and another dataset is tested. Other contributions [38,41] used a fusion of multiple constrained and unconstrained datasets for testing purposes. Moreover, some efforts have targeted a specific type of image, such as low-resolution thumbnail faces [43] and low-frequency components of the mosaic 8 × 8 images [44].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

View full text Add to dashboard Cite

Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.

show abstract

Section: Resultscontrasting

confidence: 73%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, it provided more accurate recognition on the frontal face images than those on the left and right side. Meanwhile, in [16], author proposed an automated gender classification technique that relies on CNN. This method begins with network training, which is accomplished through combining numerous face datasets retrieved from different databases including ~70000 facial images from the World Wide Web.…”

Section: Related Workmentioning

confidence: 99%

CWNN-Net: A New Convolution Wavelet Neural Network for Gender Classification using Palm Print

Elgallad¹,

Ouarda²,

Alimi³

2019

IJACSA

View full text Add to dashboard Cite

The human hand is one of the body parts with special characteristics that are unique to every individual. The distinctive features can give some information about an individual, thus, making it a suitable body part that can be relied upon for biometric identification and, specifically, gender recognition. Several studies have suggested that the hand has unique traits that help in gender classification. Human hands form part of soft biometrics as they have distinctive features that can give information about a person. Nevertheless, the information retrieved from the soft biometrics can be used to identify an individual's gender. Furthermore, the soft biometrics can be combined with the main biometrics characteristics that can improve the quality of biometric detection. Gender classification using hand features, such as palm contributes significantly to the biometric identification domain and, hence, presents itself as a valuable research topic. This study explores the use of Discrete Wavelet Transform (DWT) in gender identification, with SqueezeNet acting as a tool for unsheathing features, and Support Vector Machine (SVM) operating as discriminative classifier. Inference is made using mode voting approach. Notably, the two datasets that were crucial for the fulfillment of the study were the 11k database and CASIA. The outcome of the tests substantiated the use of voting technique for gender recognition.

show abstract

“…Nistor et al [22] also utilized Convolutional Neural Network but he conceptualizes the idea of 'Network-in-Network' by introducing the Inception module. It contains convolutional layers of size 1*1, 3*3, 5*5 and a max pooling layer.…”

Section: Gender Recognitionmentioning

confidence: 99%

A Review of Content-Based Video Retrieval Techniques for Person Identification

Ridzuan¹,

Omar²,

Sheikh³

2019

Elektrika

View full text Add to dashboard Cite

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification.

show abstract

Automatic gender recognition for “in the wild” facial images using convolutional neural networks

Cited by 6 publications

References 11 publications

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

CWNN-Net: A New Convolution Wavelet Neural Network for Gender Classification using Palm Print

A Review of Content-Based Video Retrieval Techniques for Person Identification

Contact Info

Product

Resources

About