Wavelet packet analysis for face recognition

García, Christophe; Zikos, George; Tziritas, Georgios

doi:10.1016/s0262-8856(99)00056-6

Cited by 122 publications

(43 citation statements)

References 17 publications

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“…Wavelets are basically a kind of multi determination capacity rough guess that take into account the progressive deterioration of a sign or picture. They have been connected effectively to different issues including article location [12] [13], face acknowledgment [14] and picture recovery [15]. Diverse reasons make the components separated utilizing Haar wavelets appealing for vehicle discovery.…”

Section: L: Post Processing and Enhancementmentioning

confidence: 99%

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

Gaurav

Kumar

Venkatesan

et al. 2016

MATEC Web of Conferences

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Abstract. Vehicle recognition is the chief stride in observing the speeding vehicles in a thruway. The feature arrangements caught by a stationary camera demonstrate to us that there's a requirement for a vehicle location calculation which handles sudden light change furthermore the situations where the closer view converges away from plain sight. This paper gives us a study of different foundation subtraction systems that are utilized for recognizing the vehicles effectively. Vehicles proceeding onward street are of significance on the grounds that issues like movement blockage, monetary waste, sticking on the underpasses and over-extensions (if the vehicle going through is not of the passable size) are connected with them.Index Terms-Vehicle Detection, video sequences, foreground, background, MATLAB, RGB conversion.

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Section: L: Post Processing and Enhancementmentioning

confidence: 99%

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

Gaurav

Kumar

Venkatesan

et al. 2016

MATEC Web of Conferences

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“…Recently, spherical harmonics based representation has been proposed for illumination invariant recognition [9]. Wavelet-based features have been used to obtain a representation for face recognition task in [10,11,12,13]. The use of statistical measures (mean, variance) of fully decomposed wavelet subbands or packets as a feature was reported in [10].…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet-based features have been used to obtain a representation for face recognition task in [10,11,12,13]. The use of statistical measures (mean, variance) of fully decomposed wavelet subbands or packets as a feature was reported in [10]. Here, 2D-DWT is used to fully decompose the face image and statistical features such as mean and variance are extracted from the wavelet coefficients, and used as a feature vector for representation.…”

Section: Introductionmentioning

confidence: 99%

Selection of Wavelet Subbands Using Genetic Algorithm for Face Recognition

Pathangay

Das

2006

Computer Vision, Graphics and Image Processing

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Abstract. In this paper, a novel representation called the subband face is proposed for face recognition. The subband face is generated from selected subbands obtained using wavelet decomposition of the original face image. It is surmised that certain subbands contain information that is more significant for discriminating faces than other subbands. The problem of subband selection is cast as a combinatorial optimization problem and genetic algorithm (GA) is used to find the optimum subband combination by maximizing Fisher ratio of the training features. The performance of the GA selected subband face is evaluated using three face databases and compared with other wavelet-based representations.

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“…In [2,3], mean values and the corresponding variances were computed from face images to characterize different faces. Specifically, 3 mean values and 3 variances of the approximation image and 15 variances of details images form the feature vector.…”

Section: Main Menumentioning

confidence: 99%

“…Here we present an approach for indoor object recognition using this intra-band information. Unlike experiments carried out in [2,3], we use unconstrained images of objects in a normal office. The experimental results show that these features work quite well in our application to recognize the instances of indoor objects.…”

Section: Main Menumentioning

confidence: 99%

Indoor object recognition through human interaction using wavelet features

Wang¹,

Lopes²

IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003

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In this paper a preliminary work towards grounded concept learning for a service robot through its vision and human interaction is presented. With a lifelong learning server (LLL), described in [8], the robot can incrementally learn to recognize instances of such concepts of indoor objects as "Person", "Trash-can" and "Triangle sign" using simple intra-band statistical features extracted from the Haar wavelet transform of its vision information under the instruction of a human teacher. Experimental results show that these simple waveletbased features can efficiently describe the characteristics of different objects in an office-like environment.Comparison with some other feature extraction methods is also given.

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Wavelet packet analysis for face recognition

Cited by 122 publications

References 17 publications

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

Selection of Wavelet Subbands Using Genetic Algorithm for Face Recognition

Indoor object recognition through human interaction using wavelet features

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