A Spatial Density and Phase Angle Based Correlation for Multi-type Family Photo Identification

  • Anaica Grouver
  • Palaiahnakote Shivakumara
  • Maryam Asadzadeh Kaljahi
  • Bhaarat Chetty
  • Umapada Pal
  • Tong LuEmail author
  • G. Hemantha Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12047)


Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for research community to identify multi type family photos, namely, normal family (family of the same race, religion or culture), multi-culture family (family of different culture, religion or race) from the family and non-family photos (images with friends, colleagues, etc.). In this work, we present a new method that combines spatial density information with phase angle for multi-type family photo classification. The proposed method uses three facial key points, namely, left-eye, right-eye and nose, for the features which are based on color, roughness and wrinkleless of faces, these are prominent for extracting unique cues for classification. The correlations between features of Left & Right Eyes, Left Eye & Nose and Right Eye & Nose are computed for all the faces in an image. This results in feature vectors for respective spatial density and phase angle information. Furthermore, the proposed method fuses the feature vectors and feeds them to the Convolutional Neural Network (CNN) for the classification of the above-three class problem. Experiments conducted on our database which contains three classes, namely, multi-cultural, normal and non-family images and the benchmark databases (due to Maryam et al. and Wang et al.) which contain two class-family and non-family images, show that the proposed method outperforms the existing methods in terms of classification rate for all the three databases.


Spatial density Fourier transform Phase angle Correlation coefficients Convolutional Neural Network Family and non-family photos 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anaica Grouver
    • 1
  • Palaiahnakote Shivakumara
    • 1
  • Maryam Asadzadeh Kaljahi
    • 1
  • Bhaarat Chetty
    • 2
  • Umapada Pal
    • 3
  • Tong Lu
    • 4
    Email author
  • G. Hemantha Kumar
    • 5
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Google Developers Group, NASDAQBangaloreIndia
  3. 3.Computer Vision and Pattern Recognition UnitIndian Statistical InstituteKolkataIndia
  4. 4.National Key Lab for Novel Software TechnologyNanjing UniversityNanjingChina
  5. 5.University of MysoreMysoreIndia

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