3D face detection based on salient features extraction and skin colour detection using data mining

Sayed, Abdul Rahman El; Chakik, Abdallah El; Alabboud, Hassan; Yassine, Adnan

doi:10.1080/13682199.2017.1358528

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Usually, traditional [ 17 , 18 , 19 , 20 , 21 ] and deep-learning-based face detectors [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] are designed to process 2D images, but they have also been extended to analyze 3D data [ 30 , 31 , 32 , 33 ]. These approaches take advantage of deep information to be less sensitive to low/high illumination conditions and viewpoint variation in comparison to 2D face detectors.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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“…Zhang et al [19] propose a novel multi-scale spectral-spatial gradient based salient object detection method. Syed et al [20] propose an efficient and robust method for face detection on a 3D point cloud represented by a weighted graph [21], Mu et al [22] propose a novel deep neural network framework embedded with covariance descriptor [23] for salient object detection in low-contrast images [24]. Ren et al [25] take color difference as the most important cue to detect salient objects.…”

Section: Introductionmentioning

confidence: 99%

A Unified Model for Salient Object Detection

Alzahrani¹,

Afridi²

2019

Preprint

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We propose a novel method for salient object detection in different images. Our method integrates spatial features for efficient and robust representation to capture meaningful information about the salient objects. We then train a conditional random field (CRF) using the integrated features. The trained CRF model is then used to detect salient objects during the online testing stage. We perform experiments on two standard datasets and compare the performance of our method with different reference methods. Our experiments show that our method outperforms the compared methods in terms of precision, recall, and F-Measure.

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