Proceedings ArticleDOI
Boosting local descriptors for matching composite and digital face images
Paritosh Mittal,Aishwarya Jain,Richa Singh,Mayank Vatsa +3 more
- pp 2797-2801
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TLDR
This research designs a patch based face recognition algorithm that generates patches around fiducial features and extracts local information from these patches using Daisy descriptor and is efficiently matched using GentleBoostKO algorithm.Abstract:
Sketch recognition is one of the most challenging applications of face recognition. Due to the incorrectness of features in the witness description, standard face recognition algorithms are generally not applicable to matching sketches with digital face images. This research designs a patch based face recognition algorithm that generates patches around fiducial features and extracts local information from these patches using Daisy descriptor. The information extracted from these patches are then efficiently matched using GentleBoostKO algorithm. The experiments performed on the PRIP composite face image database show that the proposed algorithm yields promising results and outperforms existing state-of-the-art algorithms and a commercial system.read more
Citations
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IEEE transactions on pattern analysis and machine intelligence
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Journal ArticleDOI
A survey on heterogeneous face recognition
TL;DR: This survey provides a comprehensive review of established techniques and recent developments in HFR, and offers a detailed account of datasets and benchmarks commonly used for evaluation.
Proceedings ArticleDOI
Composite sketch recognition via deep network - a transfer learning approach
TL;DR: In the proposed algorithm, first the deep learning architecture based facial representation is learned using large face database of photos and then the representation is updated using small problem-specific training database.
Proceedings ArticleDOI
Recognizing composite sketches with digital face images via SSD dictionary
TL;DR: The proposed algorithm utilizes a SSD based dictionary generated via 50,000 images from the CMU Multi-PIE database, and the gallery-probe feature vectors created using SSD dictionary are matched using GentleBoostKO classifier.
Journal ArticleDOI
Composite sketch recognition using saliency and attribute feedback
TL;DR: The results obtained show that the proposed algorithm improves the state-of-art in matching composite sketch and digital face images and yields the rank 50 identification accuracy of 70.3% on a database of 1500 subjects.
References
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Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.