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Individual biometrics pattern based artificial image analysis techniques

  • * Corresponding author: Israa Mohammed Khudher

    * Corresponding author: Israa Mohammed Khudher 
Abstract / Introduction Full Text(HTML) Figure(4) / Table(5) Related Papers Cited by
  • Biometric characteristics have been used since antiquated decades, particularly in the detection of crimes and investigations. The rapid development in image processing made great progress in biometric features recognition that is used in all life directions, especially when these features recognition is constructed as a computer system. The target of this research is to set up a left foot biometric system by hybridization between image processing and artificial bee colony (ABC) for feature choice that is addressed within artificial image processing. The algorithm is new because of the rare availability of hybridization algorithms in the literature of footprint recognition with the artificial bee colony assessment. The suggested system is tested on a live-captured ninety colored footprint images that composed the visual database. Then the constructed database was classified into nine clusters and normalized to be used at the advanced stages. Features database is constructed from the visual database off-line. The system starts with a comparison operation between the foot-tip image features extracted on-line and the visual database features. The outcome from this process is either a reject or an acceptance message. The results of the proposed work reflect the accuracy and integrity of the output. That is affected by the perfect choice of features as well as the use of artificial bee colony and data clustering which decreased the complexity and later raised the recognition rate to 100%. Our outcomes show the precision of our proposed procedures over others' methods in the field of biometric acknowledgment.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


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  • Figure 1.  Bee Colony procedure

    Figure 2.  A sample from the visual database

    Figure 3.  A sample from the visual database

    Figure 4.  The system outcome for acceptance message

    Table 1.  Summary of biometric features related papers

    Biometric Type Technique Performance Version
    Foot-tip Morphology, statistical 83.38 - 89.52 2019 [13]
    Foot-tip Modified Sequential Haar Energy Transform (MSHET) 92.37 2019 [19]
    Foot-tip texture and shape 99 2016 [26]
    Foot-tip Modified Haar Energy (MHE) 93 2016 [36]
    Foot-tip (ABC) Algorithm for finding the curve fitting 97.15 2010 [14]
    Lung image (ABC) to segment clinical 99.2 2016 [12]
    Ear print Extracting the most discriminant key-points 99.6 2010 [17]
    Foot-tip Fuzzy neural network 90-92.80 2010 [36]
    Foot-tip Suggested work Statistical Chain code-based (ABC) 100 2020
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    Table 2.  Features description

    Moment title Equation Parameter
    Mean $ M_i = \frac{1}{N} \sum_{j=1}^{N} fit_i $ (5) Where $ f_ij $ is the value of the feature and N Denotes features frequency [27].
    Description This factor is directly proportional to brightness. The big value the more brightness and vice versa [27].
    Moment title Equation Parameter
    Standard Deviation (STD) $ \varsigma = (\frac{1}{N} \sum_{j=1}^{n} (f_i - M_i)^2)^{\frac{1}{2}} $ (6) Where Mi represents the average of the image, $ f_ij $ denotes the value of the feature and N reflects the observation size [27].
    Description This factor is inversely proportional to image contrast. The big value the small contrast and vice versa [27].
    Center-Angle $ \Theta = | atan^2 (y, x) \pi / 180 $ (7) y, x are convoluted images with specific masks[27].
    Description This factor describes the direction of the chain code.
    Mean, Std of Histogram of the Chain Code = =
    Description These factors describe the intensity and contrast of the Chain Code [22]
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    Table 3.  The experimental results

    Query name Bestsolution Image number Cluster Time in sec
    Qry1 0 4 1 0.2888
    Qry2 0.0740 13 2 0.1215
    Qyr3 0.2932 28 3 0.1318
    Qry4 0 31 4 0.1261
    Qry5 0.0765 42 5 0.1196
    Qry6 1.6607 52 6 0.1215
    Qry7 0 61 7 0.1222
    Qry8 0 71 8 0.1204
    Qry9 0 82 9 0.1207
    Qry10 0 83 9 0.1210
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    Table 4.  Enhancement metrics

    Metric Value
    Accurecy 100%
    Confidence 100%
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    Table 5.  Enhancement analysis of related work

    Moment title Equation Parameter
    Technique Author Accuracy Rate %
    (ABC) Algorithm for finding the curve fitting and Euclidean K. K. Nagwanshi and S. Dubey [14] 97.15
    distance for foot-tip K. K. Nagwanshi and S. Dubey [15] 85
    Fuzzy Neural Networks + Geometrical Characteristics W. Rong et al. [40] 90-92.80
    (ABC) for Area Allocation L. Yang et al. [18] 67.4-67.7
    (ABC) for object recognition C. Chidambaram and H. S. Lopes [3] 88-99
    (ABC)+Fuzzy C Mean M. Shokouhifar and G. S. Abkenar [24] 98.38
    (ABC) for Handwritten recognition S. Nebti and A. Boukerram [29] 99.82
    The suggested paper (ABC)+Chain code+ statistical features Israa M. Kh., et.al. 100
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