Deep Face Rectification for 360° Dual-Fisheye Cameras

Li, Yi-Hsin; Lo, I-Chan; Chen, Homer H.

doi:10.1109/tip.2020.3019661

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…Ref. [ 10 ] proposed a combination of different networks to restore the linear geometry of the face, thus reducing the effect of image distortion on detection. In the [ 11 ], an automatic correction method for omnidirectional image distortion based on a unified learning model (OIDC-Net) is proposed, which uses an attention mechanism with heterogeneous distortion coefficient estimation to achieve the correction of omnidirectional images.…”

Section: Related Workmentioning

confidence: 99%

Glass Refraction Distortion Object Detection via Abstract Features

Cai

Chen

Sun

et al. 2022

Computational Intelligence and Neuroscience

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Glass reflection and refraction lead to missing and distorted object feature data, affecting the accuracy of object detection. In order to solve the above problems, this paper proposed a glass refraction distortion object detection via abstract features. The number of parameters of the algorithm is reduced by introducing skip connections and expansion modules with different expansion rates. The abstract feature information of the object is extracted by binary cross-entropy loss. Meanwhile, the abstract feature distance between the object domain and source domain is reduced by a loss function, which improves the accuracy of object detection under glass interference. To verify the effectiveness of the algorithm in this paper, the GRI dataset is produced and made public on GitHub. The algorithm of this paper is compared with the current state-of-the-art Deep Face, VGG Face, TBE-CNN, DA-GAN, PEN-3D, LMZMPM, and the average detection accuracy of our algorithm is 92.57% at the highest, and the number of parameters is only 5.13 M.

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Section: Related Workmentioning

confidence: 99%

Glass Refraction Distortion Object Detection via Abstract Features

Cai

Chen

Sun

et al. 2022

Computational Intelligence and Neuroscience

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“…With respect to traditional techniques [20][21][22], the equirectangular projection introduces position-dependent distortions which must be considered when devising specific algorithms. This is a very new open issue, but some works have already been published [23,24]. To the best of our knowledge, there are no available datasets of videos acquired by an omnidirectional camera network (that is one of our future works), which are necessary in object re-identification applications [25].…”

Section: • Videos Datasetsmentioning

confidence: 99%

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

et al. 2021

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Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.

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“…Authors in [8] suggested thinking about feature representation invariant to pose in recognizing in surveillance videos. Nevertheless, rotated face recognition remains a challenge in practical scenarios [9][10][11]. The rotation invariant detection capability using various methodologies is summarized in Tabs.…”

Section: Introductionmentioning

confidence: 99%

“…RIFDS uses binary images to display the selected facial features. When a test image is uploaded, it is converted into a grayscale image because image color increases the complexity of multiple color channels (like RGB and CMYK) [9]. RIFDS is tested on the face databases, namely JAFFF, ORL, CMU, MIT-CBCL, and LFW.…”

Section: Introductionmentioning

confidence: 99%

Secure Rotation Invariant Face Detection System for Authentication

Verma¹,

Baljon²,

Mishra³

et al. 2022

Computers, Materials &Amp; Continua

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Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar Harmonic Transforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.

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Deep Face Rectification for 360° Dual-Fisheye Cameras

Cited by 11 publications

References 27 publications

Glass Refraction Distortion Object Detection via Abstract Features

Glass Refraction Distortion Object Detection via Abstract Features

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

Secure Rotation Invariant Face Detection System for Authentication

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