1 |
2001 |
P. Viola and M. Jones |
Rapid object detection using a boosted cascade of simple features [10] |
Machine Learning Approach |
Visual object detection that is capable of process images extremely speedily and achieving high detection rates |
2 |
2009 |
J. Mairal, F. Bach, J. Ponce, and G. Sapiro, |
Online dictionary learning for sparse coding [1] |
Online optimization algorithm based on stochastic approximations. |
The retrieval system need more efficient algorithm with accuracy for huge database |
3 |
2010 |
Z. Wu and H. Shum |
Scalable Face Image Retrieval with Identity-Based Quantization and Multireference Reranking [11] |
Inverted index based on local features |
Face images with good recall, while the multi-reference re-ranking with global hamming signature leads to good precision. |
4 |
2011 |
R. S. Feris and L. S. Davis |
Image Ranking and Retrieval based on Multi-Attribute Queries [8] |
Multi-attribute query based |
Attributes are independent both single object and multiple object categories |
5 |
2011 |
Y.-H. Lei, Y.-Y. Chen, L. Iida, B.-C. Chen, H.-H. Su, and W. H. Hsu |
Photo search by face positions and facial attributes on touch devices [4] |
Block-based indexing approach |
It achieved high performance but the database of the image increases as storage capacity of the server’s increases. The size of database leads for the more new algorithm in retrieval system |
6 |
2012 |
S. Murala, R. P. Maheshwari, and R. Balasubramanian |
Local tetra patterns: A new feature descriptor for content-based image retrieval [2] |
LTrPs method |
The retrieval system need more efficient algorithm with accuracy for huge database. |
7 |
2012 |
W. J. Scheirer, N. Kumar, P. N. Belhumeur, and T. E. Boult |
Multi-attribute spaces: Calibration for attribute fusion and similarity search [3] |
To construct normalized “multi-attribute spaces” from raw classifier outputs, using statistical Extreme Value Theory |
The database of the image increases as storage capacity of the servers increases. The size of database leads for the more new algorithm in retrieval system. |
8 |
2013 |
B. C. Chen, Y. Y. Chen, Y. H. Kuo, and W. H. Hsu |
Scalable face image retrieval using attribute-enhanced sparse codewords [15] |
Attribute-enhanced sparse coding and Attribute embedded inverted indexing |
In this paper, proposed automatically detected human attributes to improve content based face retrieval by constructing semantic codewords. |
9 |
2013 |
X. Shen, Z. Lin, J. Brandt, and Y. Wu |
Detecting and aligning faces by image retrieval [7] |
voting-based method |
Faces may be detected by choosing the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. |
10 |
2014 |
S. Suchitra, S. Chitrakala, and J. Nithya |
A Robust Face Recognition using Automatically Detected Facial Attributes [9] |
Local Octal Pattern (LOP) feature descriptor with automatic facial attributes |
In this paper, facial feature extraction done by LOP with automatic facial attributes. It improved the accuracy of the face retrieval system but still some of the attributes will decrease the performance |
11 |
2014 |
I.Sudha, V.Saradha, M.Tamilselvi, D.Vennila |
Face Image Retrieval Using Facial Attributes By K-Means [19] |
Facial Attributes By K-Means algorithm |
It achieved immediate retrieval in a very large-scale dataset by raising the face retrieval in the offline and online stages. The proposed automatic method allows the recognition and their time characteristics (i.e., sequences of time segments: neutral, start, height, and balance). |
12 |
2015 |
Jun Liu, Xiaojun Jing, Songlin Sun, Zifeng Lian |
Local Gabor Dominant Direction Pattern for Face Recognition [24] |
Local gabor dominant direction pattern (LGDDP) |
The LGDDP convolve query images with Gabor filters to produce resultant images of multiple scales. The output images contain pixels encoded with LGDDP. In addition, nearest neighbour classifier help classify face images. |
13 |
2016 |
J.H. Na, H.J. Chang |
Blockwise collaborative representation-based classification via L2-norm of query data for accurate face recognition [25] |
Blockwise collaborative representation-based classification |
A blockwise collaborative along with L
2-norm data set propose for face recognition from images. The image divides into blocks for representation coefficients estimations. In addition, conventional reconstruction method employ for classification of query image. |
14 |
2016 |
Cuiming Zou, Kit Ian Kou, Yulong Wang |
Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition [26] |
Quaternion Collaborative and Sparse Representation |
Quaternion CRC (QCRC) in addition with quaternion SRC (QSRC) using quaternion ℓ1minimization employ for face recognition. The QCRC and QSRC perform better than CRC and SRC method. The CRC and SRC method use for face recognition from gray scale image. |
15 |
2016 |
Yang Hui-xian, Cai Yong-yong. |
Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination [27] |
An improved version of AWOGBP and PCA method |
An improved version of AWOGBP and PCA method overcome illumination barrier in precise face recognition. |
16 |
2016 |
Tomás Mantecón, Carlos R. del-Blanco, Fernando Jaureguizar, Narciso García |
Visual Face Recognition Using Bag of Dense Derivative Depth Patterns[28] |
Dense Derivative Depth Patterns |
The authors propose depth camera to discriminate face with high accuracy. The face images recognize faces with different pose in all directions. |