Adapting Local Features for Face Detection in Thermal Image

Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin Ichiro

doi:10.3390/s17122741

Cited by 27 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Infrared imaging takes advantage of the generally constant distribution of face temperatures to achieve more reliable detection. Work [14] proposed the application of AdaBoost to a mixture of local features like Haar-like, MB-LBP and (Histogram of Oriented Gradient) HOG to detect faces captured in infrared cameras. MB-LBP was extended by fitting a margin around the reference giving better noise immunity.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

Dandashy¹,

Hassan²,

Bitar³

2019

IJEMR

View full text Add to dashboard Cite

The effective real-time face detection framework proposed by Viola and Jones gained much popularity due its computational efficiency and its simplicity. A notable variant replaces the original Haar-like features with MB-LBP (Multi-Block Local Binary Pattern) which are defined by the local binary pattern operator, both detector types are integrated into the OpenCV library. However, each descriptor and its evaluation method has its own set of strengths and setbacks. In this paper, an enhanced two-layer face detector composed of both Haar-like and MB-LBP features is presented. Haar-like features are employed as a coarse filter but with a new evaluation involving dual threshold. The already established MB-LBPs are arranged as the fine filter of the detector. The Gentle AdaBoost learning algorithm is deployed for the training of the proposed detector to reach the classification and performance potential. Experiments show that in the early stages of classification, Haar features with dual threshold are more discriminative than MB-LBP and original Haarlike features with respect to number of features required and computation. Benchmarking the proposed detector demonstrate overall 12% higher detection rate at 17% false alarm over using MB-LBP features singly while performing with ×3 speedup.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

Dandashy¹,

Hassan²,

Bitar³

2019

IJEMR

View full text Add to dashboard Cite

show abstract

“…Because of this problem, the single camera-based method, which does not require calibration between cameras, has been studied. Among the existing single camera-based methods, the authors in [ 7 , 8 , 9 ] conducted face detection using only one thermal camera. Zin et al proposed three face detection methods, among which the performance of the nighttime face detection using a multi-slit method was the highest [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, if the position and angle of the camera change, the parameters need to be updated. The authors in [ 8 , 9 ] used adaboost algorithms based on various hand-crafted features. Agrawal et al [ 8 ] developed a face detection that performs a decision-level fusion of two different adaboost results by using Haar-like features and LBP features, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, a near-infrared (NIR) light camera or a thermal camera has been used in most existing nighttime face detection studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In contrast to the visible-light camera, NIR and thermal cameras use infrared (IR) wavelengths; therefore, they are robust to ambient lighting changes and low intensity of illumination.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Cho

Baek

Kim

et al. 2018

Sensors

View full text Add to dashboard Cite

Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.

show abstract

“…According to comparative results, the larger pixel is labelled as 1 and the smaller is labelled as 0. The binary value is converted to the decimal CS-LBP value [21][22][23]. The calculation process of CS-LBP features is shown in Figure 1.…”

Section: Center-symmetric Local Binary Patterns (Cs-lbp)mentioning

confidence: 99%

The Face Recognition Method Based on CS-LBP and DBN

Sun

Yin

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

show abstract

Adapting Local Features for Face Detection in Thermal Image

Cited by 27 publications

References 22 publications

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

The Face Recognition Method Based on CS-LBP and DBN

Contact Info

Product

Resources

About