Reference Face Graph for Face Recognition

Kafai, Mehran; An, Le; Bhanu, Bir

doi:10.1109/tifs.2014.2359548

Cited by 46 publications

(16 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A reference face graph (RFG) [8] is a composition of nodes and also the dyadic interactions (edges) involving the nodes. A reference experience is a node addressing just one individual while in the guide encounter graph.…”

Section: Reference Face Graphmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Unconstrained Face Recognition Algorithm and Its Applications

Tyagi¹,

Tomar²,

Baik³

2016

IJSIA

View full text Add to dashboard Cite

show abstract

Section: Reference Face Graphmentioning

confidence: 99%

“…Misalignment: Since the looks usually are detected by an automated face alarm [8], [19], the cropped looks are not aimed. Nonetheless, because stance is required by many attribute descriptors before element removal, imbalance degrades the performance of a face-recognition system.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Unconstrained Face Recognition Algorithm and Its Applications

Tyagi¹,

Tomar²,

Baik³

2016

IJSIA

View full text Add to dashboard Cite

show abstract

“…Different methods can be used to select a subset from the entire reference candidate pool in order to maintain discriminality while reducing redundancy for better efficiency. For example, in [45] for face recognition, reference set selection is proposed from a low-rank decomposition point of view. In [46] for biometric pattern retrieval, rule-based methods are suggested for reference set selection, including max-variation, max-mean, and min-correlation.…”

Section: F Reference Set Analysismentioning

confidence: 99%

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Chen

Bhanu

2015

IEEE Sensors J.

Self Cite

View full text Add to dashboard Cite

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments.

show abstract

“…In the prior literature, plenty of research efforts have been dedicated to the face recognition problem and tremendous progress has been achieved [Zhang et al 1997;Tenenbaum et al 2000;Belkin and Niyogi 2003;Zhang et al 2011;Kafai et al 2014]. …”

Section: Introductionmentioning

confidence: 99%

Local Structure-Based Sparse Representation for Face Recognition

Liu

Tang

Song

et al. 2015

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

This article presents a simple yet effective face recognition method, called local structure-based sparse representation classification (LS_SRC). Motivated by the "divide-and-conquer" strategy, we first divide the face into local blocks and classify each local block, then integrate all the classification results to make the final decision. To classify each local block, we further divide each block into several overlapped local patches and assume that these local patches lie in a linear subspace. This subspace assumption reflects the local structure relationship of the overlapped patches, making sparse representation-based classification (SRC) feasible even when encountering the single-sample-per-person (SSPP) problem. To lighten the computing burden of LS_SRC, we further propose the local structure-based collaborative representation classification (LS_CRC). Moreover, the performance of LS_SRC and LS_CRC can be further improved by using the confusion matrix of the classifier. Experimental results on four public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to occlusion; little pose variation; and the variations of expression, illumination, and time. . 2015. Local structure-based sparse representation for face recognition.

show abstract

Reference Face Graph for Face Recognition

Cited by 46 publications

References 52 publications

A Survey of Unconstrained Face Recognition Algorithm and Its Applications

A Survey of Unconstrained Face Recognition Algorithm and Its Applications

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Local Structure-Based Sparse Representation for Face Recognition

Contact Info

Product

Resources

About