Enhancing face recognition at a distance using super resolution

Al-Hassan, Nadia; Jassim, Sabah; Sellahewa, Harin

doi:10.1145/2361407.2361429

Cited by 9 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike other methods, like in [13], that involve using Super Resolution for reconstructing LR probe images to match them with HR gallery images, this work enhances the resolution of both probe and gallery LR images.…”

Section: B Experiments and Results Analysismentioning

confidence: 99%

Interpolation of low resolution images for improved accuracy in human face recognition

Elazhari

Ahmadi

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

View full text Add to dashboard Cite

This paper presents the effect of interpolation schemes, namely, nearest-neighbor, bilinear, and bicubic, when applied to low-resolution images as a preprocessing step for improving the recognition rate in human face recognition system. Three feature extraction methods are used, namely, Local Binary Pattern, Discrete Wavelet Transform, and Block-Based Discrete Cosine Transform, with and without interpolation for comparison purpose. The experiments are conducted on the ORL database. Bicubic and bilinear improve the recognition rate of low resolution images considerably. NTRODUCTION Human face recognition (FR) constitutes a very active area of research for the last two decades and many challenges have been addressed, such as, illumination variation, pose variation, local shadow etc. however, in real-world applications, such as, video surveillance cameras, where it is often difficult to obtain good quality recordings of observed human faces, face recognition is still challenging task. One important factor evaluating the quality of the recordings is the resolution of the images. Human face recognition from low resolution (LR) images has been tackled by many researchers [1]- [4]. In this paper we have utilized three different interpolation techniques as a preprocessing step for improving the quality of lowresolution images before feature extraction as will be discussed in section 3. It is pointed out that image-based (holistic) approach is superior for LR face recognition [1]. Thus, we have chosen three effective feature extractors, namely, Local Binary Pattern (LBP) [5], Discrete Wavelet Transform (DWT) based PCA [6-8], and Block-Based Discrete Cosine Transform (BBDCT) [9] for our experiments and the classifier used is Euclidean classifier.This paper is organized as follows. Section 2 is an overview of the face descriptors that will be used. Interpolation techniques illustrated In Section 3, and experiments and results are discussed in section 4 and concluding remarks are given in section 5. FEATURE EXTRACTIONThis section outlines the feature extraction schemes that are used in this paper: LBPThis operator labels the pixels of an image by thresholding the 3×3 neighbourhood of each pixel with the center value and considering the result as a binary number (see Figure 1). Then the histogram of the labels can be used as a texture descriptor. Using circular neighbourhoods and bilinearly interpolating the pixel values allow any radius and number of pixels in the neighbourhood. Further extension of LBP is to use uniform patterns. A uniform pattern has at most two bitwise transitions from 0 to 1 or vice versa when the binary string is considered circular. In this work, we adopt a LBP operator , , that selects uniform patterns in (8,1) neighbourhood; where the radius is one with 8 neighbors. It is observed that uniform patterns account for nearly 90% of all patterns in the (8, 1) [5]. We select Chi square statistic ( ) as the dissimilarity measure for histogramWhere S and M are two LBP histograms; , is the histogram of reg...

show abstract

Section: B Experiments and Results Analysismentioning

confidence: 99%

Interpolation of low resolution images for improved accuracy in human face recognition

Elazhari

Ahmadi

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

View full text Add to dashboard Cite

show abstract

“…The nth iteration output the nth version of the HR image simply by calculating x (n) =(x (n-1) +x e ), representing the back projection of the difference (y-y (n) ) onto x (n-1) . The SR scheme terminates either when the energy of the error term (y-y (n) ) is reduced below a certain threshold or the number of iterations reached a fixed maximum number [8]. Variants of the IBP expand each iteration by pack-projecting additional terms, representing high frequency information in x 0 e.g.…”

Section: Super Resolutionmentioning

confidence: 99%

Face Recognition in Uncontrolled Condition: Can Compressive Sen sing and Super-Resolution Meet the Challenge?

Jassim¹

2013

J Biomet Biostat

View full text Add to dashboard Cite

This paper is concerned with face recognition under uncontrolled conditions, e.g. at a distance in surveillance scenarios, and post-rioting forensic, whereby captured face images are severely degraded/blurred and of low resolution. This is a tough challenge due to many factors including difficulties in determining a model for image degradation that encompasses a range of realistic capturing conditions. We present the results of our investigations into recently developed Compressive Sensing (CS) theory to develop scalable face recognition schemes using a variety of overcomplete dictionaries that construct super-resolved face images from any input low-resolution degraded face image. We shall demonstrate that deterministic as well as non-deterministic dictionaries that do not involve the use of face image information but satisfy some form of the Restricted Isometry Property (RIP) used for CS can achieve face recognition accuracy levels as good a, if not better than, those achieved by dictionaries, proposed in the literature, that are learnt from face image databases using elaborate procedures. We shall elaborate on how this approach helps in crime fighting and terrorism.

show abstract

“…FRD presents number of challenges including varied illumination, non-cooperative pose, limited field of view, out of focus imaging among many others in recognizing the subject due to unconstrained capture. Some of the earlier works have used new sensors [17] [6] or proposed new algorithms to improve the acquired image quality [1]. However, proposed algorithms are closely coupled to the sensors employed for acquiring the samples.…”

Section: Introductionmentioning

confidence: 99%

Improved face recognition at a distance using light field camera & super resolution schemes

Raghavendra

Raja

Yang

et al. 2013

Proceedings of the 6th International Conference on Security of Information and Networks

View full text Add to dashboard Cite

In this paper, we present an empirical study on exploring the Light Field Camera (LFC) for identifying multiple faces present at different distance. Since LFC can render multiple focus images in single exposure, one can combine these multiple images to obtain single all-in-focus image. Thus the constructed all-in-focus image will have all regions in focus and hence allows one to capture more information about the subject present even at a far distance. At the same time one can also construct the super resolution image to further improve the face recognition at a distance. Thus, in this work, we explore both all-in-focus and super resolution schemes to evaluate the multiple face recognition at a distance using LFC. We carry out extensive experiments on light field face dataset and present both qualitative and quantitative results

show abstract

Enhancing face recognition at a distance using super resolution

Cited by 9 publications

References 25 publications

Interpolation of low resolution images for improved accuracy in human face recognition

Interpolation of low resolution images for improved accuracy in human face recognition

Face Recognition in Uncontrolled Condition: Can Compressive Sen sing and Super-Resolution Meet the Challenge?

Improved face recognition at a distance using light field camera & super resolution schemes

Contact Info

Product

Resources

About