Deity face recognition using schur decomposition and hausdorff distance measure

Ramasamy, Balakrishnan; Kannan, Rajkumar; Kannaiyan, Suresh; Swaminathan, Sridhar

doi:10.1109/mwscas.2013.6674865

Cited by 3 publications

(4 citation statements)

References 24 publications

(26 reference statements)

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“…However, the Eigenface approach sometimes misclassifies face images with different illumination, pose and rotation effects. To handle this, our prior work in [41] is used for illumination and pose invariant face recognition. The face recognition approach in [41] achieves 93.2% recognition accuracy on IDES dataset that contains face images with different illumination and pose variations, where the Eigenface approach attains recognition accuracy only about 78.4% on the IDES dataset.…”

Section: Pre-processingmentioning

confidence: 99%

“…To handle this, our prior work in [41] is used for illumination and pose invariant face recognition. The face recognition approach in [41] achieves 93.2% recognition accuracy on IDES dataset that contains face images with different illumination and pose variations, where the Eigenface approach attains recognition accuracy only about 78.4% on the IDES dataset. Here, the face images were extracted from actors' images that are crawled from the web, to train the face recognizer.…”

Section: Pre-processingmentioning

confidence: 99%

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What do you wish to see? A summarization system for movies based on user preferences

Kannan

Ghinea

Swaminathan³

2015

Information Processing & Management

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Abstract. Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.

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Section: Pre-processingmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

What do you wish to see? A summarization system for movies based on user preferences

Kannan

Ghinea

Swaminathan³

2015

Information Processing & Management

Self Cite

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“….,y m in R l (l n), where y i represents x i and y i = A T x i . The objective function of the LPP is, min ij (y i -y j ) 2 S ij (9) where y i is the one dimensional representation of the original data x i and S is a similarity matrix as defined below:…”

Section: Laplacianfaces For Face Representationmentioning

confidence: 99%

“…It handles the defective matrices while computing the linearly independent eigenvalues and eigenvectors. The deity face recognition by Balakrishnan et al [9] is a recently proposed face recognition approach that uses the schur decomposition with PCA and their experiments showed the discriminant power of the schur decomposition for face recognition.…”

mentioning

confidence: 99%

Gradient-Orientation-Based PCA Subspace for Novel Face Recognition

Ghinea

Kannan

Kannaiyan³

2014

IEEE Access

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Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.

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