A New Face Recognition Technique by Landmark-Based PCA Modeled Scheme in Unconstrained Environment

  • Naresh KumarEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


There is ever scope of real-time face recognition in various applications ranging from social to commercial sectors. The methods of face recognition for these applications show considerably good performances in restricted conditions. The sounding benchmark demonstrates that all these methods are not adequate for randomly changing environment. The limitations of holistic method divert the researches’ attention to more local features and largely robust set of non-monotonic distortions. In this paper, an attempt is made to develop a method for face recognition that refines the recognition rate in the unrestricted environment. The originality derives from weight generation are considered the landmarks in facial images by computing the ratio of inter-variance to the intra-variance. The cumulative sum of weights supplements to find the distinctive features in the facial images and thus the matched person get the highest weight. Our experimental results show that the proposed framework achieves better accuracy for recognition of face than others highlighted techniques on the globally accessible challenging database.


Linear discriminant analysis (LDA) Scale-invariant feature transform (SIFT) Principal component analysis (PCA) Local binary patterns (LBP) Histogram of oriented gradient (HOG) 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.IEEE & Computer Society of India (CSI), Department of MathematicsIndian Institute of TechnologyRoorkeeIndia

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