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Who are My Family Members? A Solution Based on Image Processing and Machine Learning

Matthew R. Kiley

Computer Science Department, Southern Connecticut State University, 501 Crescent St., New Haven, CT 06515, USA

E-mail Address: kileym4@southernct.edu

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Md Shafaeat Hossain

Computer Science Department, Southern Connecticut State University, 501 Crescent St., New Haven, CT 06515, USA

E-mail Address: hossainm3@southernct.edu

Corresponding author.

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https://doi.org/10.1142/S0219467820500333Cited by:1 (Source: Crossref)

Abstract

Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors.

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