Gender Classification by Deep Learning on Millions of Weakly Labelled Images

Attique

et al. 2020

Sensors

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

Section: Gender Classificationmentioning

confidence: 99%

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Attique

et al. 2020

Sensors

“…More recently, CNNs have been introduced to NR-IQA and achieved state-of-the-art results [10,11,13]. It has also been shown that the depth of CNNs plays an important role in feature extraction [7,8,22,23]. The CNN architecture used in our work consists of ten convolutional layers, which is deeper than prior works, referred as Deep CNN (DCNN) in this paper.…”

Section: Introductionmentioning

confidence: 98%

“…But it is difficult to tranlate CNN features into a human-readable format. Extensive works [7,8,22,23] have shown a CNN architecture with more layers can deliver a better featre extraction. Our proposed CNN architecture is similar to [22] that we stack ten convolutional layers with small receptive fields for NR-IQA.…”

Section: Dcnn Architecturementioning

confidence: 99%

“…Therefore how to extract discriminant features is a common question between vision recognition tasks and training-based IQA. It has been shown that CNN-learned features outperform hand-designed ones (local binary patterns or scale-invariant feature transform) in many areas, such as object classification [7], face gender recognition [8] or fashion detection [9]. More recently, CNNs have been introduced to NR-IQA and achieved state-of-the-art results [10,11,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Saliency-based deep convolutional neural network for no-reference image quality assessment

Jia

Zhang

2017

Multimed Tools Appl

Self Cite

In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets.

Advances in Intelligent Data Analysis XVI

“…Deep neural networks deliver state of the art performance in different areas of AI [14,21,23], particularly in computer vision, and promise to be deployed in many further domains [19]. However, for all their convenience, they do attract the criticism that they operate as black boxes [1]: that they can only pick up correlations, with no regard for causality or other theoretical frameworks that humans would consider more explainable.…”

Section: Introductionmentioning

confidence: 99%

Freudian Slips: Analysing the Internal Representations of a Neural Network from Its Mistakes

Jia

Lansdall-Welfare²,

Cristianini

2017

Self Cite

The use of deep networks has improved the state of the art in various domains of AI, making practical applications possible. At the same time, there are increasing calls to make learning systems more transparent and explainable, due to concerns that they might develop biases in their internal representations that might lead to unintended discrimination, when applied to sensitive personal decisions. The use of vast subsymbolic distributed representations has made this task very difficult. We suggest that we can learn a lot about the biases and the internal representations of a deep network without having to unravel its connections, but by adopting the old psychological approach of analysing its slips of the tongue. We demonstrate in a practical example that an analysis of the confusion matrix can reveal that a CNN has represented a biological task in a way that reflects our understanding of taxonomy, inferring more structure than it was requested to by the training algorithm. In particular, we show how a CNN trained to recognise animal families, contains also higher order information about taxa such as the superfamily, parvorder, suborder and order for example. We speculate that various forms of psycho-metric testing for neural networks might provide us insight about their inner workings.