Deep Learning on Binary Patterns for Face Recognition

Vinay, A.; Gupta, Abhijay; Bharadwaj, Aprameya; Srinivasan, Arvind; Murthy, K. N. Balasubramanya; Natarajan, S.

doi:10.1016/j.procs.2018.05.164

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Furthermore, better building image processing could be obtained by using new generation deep convolutional neural networks like those described in Cevallos et al (2019) and Mayya et al (2016). Image pre-processing to remove noise and unwanted features could also be useful to enhance contour closure and building component results (see, for example, Vinay et al, 2018). Using the same methodology our intention is to extend this system to a much wider range of information that could then, using available APIs, be used to populate semantically-rich IFC BIM models in a quick and inexpensive way.…”

Section: -Conclusion Discussion and Future Workmentioning

confidence: 99%

Automatic generation of building information models from digitized plans

Doukari

Greenwood

2020

Automation in Construction

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This paper proposes a new approach to creating Building Information (BIM) models of existing buildings from digitized images. This automatic approach is based on three main steps. The first involves extracting the useful information automatically from rasterized plans by using image processing techniques that include segmentation, filtering, dilation, erosion, and contour detection. This information feeds the knowledge base of an expert system for BIM model generation. In the second step, using the knowledge base of the expert system, the information required to inform the BIM model can be deduced. The range of information thus obtainable can be extended beyond the examples given. The paper concludes with a discussion of the final stage: the automatic generation of an Industry Foundation Classes (IFC) information model with all the desired geometric, physical and technical information. This can be accomplished by using one of the available open-source application program interfaces (APIs). This stage is currently work-in-progress and will be the subject of a future publication.

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Section: -Conclusion Discussion and Future Workmentioning

confidence: 99%

Automatic generation of building information models from digitized plans

Doukari

Greenwood

2020

Automation in Construction

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“…LIFT + ELM [ 42 ], LIFT + RVFL [ 42 ], PCA-ANN [ 43 ] and LDA-ANN [ 43 ] attain RR of 93.09%, 93.58%, 98.50% and 98.50% when =16. DLOBP [ 44 ] carry RR of 93.60% on =15. ILD outstrip RR of all methods.…”

Section: Methodsmentioning

confidence: 99%

Improved local descriptor (ILD): a novel fusion method in face recognition

Karanwal

2023

Int. j. inf. tecnol.

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Literature suggests that by fusing multiple features there is immense improvement in the recognition rates as compared to the recognition rates of single descriptor. This motivate researchers to develop more and more fused descriptors by joining multiple features. Inspiring from the literature work, the proposed work launch novel local descriptor so-called Improved Local Descriptor (ILD), by joining features of 4 local descriptors. These are LBP, ELBP, MBP and LPQ. LBP captures local details. ELBP capture robust features in horizontal and vertical directions (elliptically) by using 3 × 5 and 5 × 3 patches. MBP minimizes image noise by median comparison to all the pixels and LPQ quantize the frequency components for obtaining feature size. These essential merits of 4 descriptors are encapsulated in one framework in the form of histogram feature. PCA is used further for compression and SVMs and NN are used for classification. Results on ORL, GT and Faces94 confirms strength of ILD, which beats separately implemented descriptors and various benchmark methods.

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“…Chen et al [33] proposed a hierarchical clustering-based spectrum band selection method which mitigates the influence of noise and extracts features from each spectra band by using the Gabor filter and the histograms of oriented gradients algorithm Ling et al [34] proposed a self-residual attention-based convolutionary neural network (SRANet) for discriminative face embedding feature, which aims to learn the long-range dependencies of face images by reducing the redundancy of information between channels and concentrating on the most informative components of space function maps. Vinay et al [35] proposed a robust method for real-time face recognition. Li et al [10] propose a cloud-based ubiquitous monitoring system via face recognition.…”

Section: Global Approachmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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Deep Learning on Binary Patterns for Face Recognition

Cited by 13 publications

References 15 publications

Automatic generation of building information models from digitized plans

Automatic generation of building information models from digitized plans

Improved local descriptor (ILD): a novel fusion method in face recognition

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

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