Abstract
Distinguishing identical twins using their face images is a challenge in biometrics. The goal of this study is to construct a biometric system that is able to give the correct matching decision for the recognition of identical twins. We propose a method that uses feature-level fusion, score-level fusion, and decision-level fusion with principal component analysis, histogram of oriented gradients, and local binary patterns feature extractors. In the experiments, face images of identical twins from ND-TWINS-2009-2010 database were used. The results show that the proposed method is better than the state-of-the-art methods for distinguishing identical twins. Variations in illumination, expression, gender, and age of identical twins’ faces were also considered in this study. The experimental results of all variation cases demonstrated that the most effective method to distinguish identical twins is the proposed method compared to the other approaches implemented in this study. The lowest equal error rates of identical twins recognition that are achieved using the proposed method are 2.07% for natural expression, 0.0% for smiling expression, and 2.2% for controlled illumination compared to 4.5, 4.2, and 4.7% equal error rates of the best state-of-the-art algorithm under the same conditions. Additionally, the proposed method is compared with the other methods for non-twins using the same database and standard FERET subsets. The results achieved by the proposed method for non-twins identification are also better than all the other methods under expression, illumination, and aging variations.
Highlights
Biometrics has recently been widely used for human recognition in many different countries to identify a person under controlled or uncontrolled environments
6.6 Results and discussion All the experimental results demonstrate that the decision fusion (PCA, histograms of oriented gradients (HOG), local binary patterns (LBP)) is better than or comparable with the state-of-the-art methods
The proposed method is better than the decision fusion (PCA, HOG, LBP), and it shows superior performance compared to the state-of-the-art methods in this field for all types of experimental conditions including expression, illumination, gender, and age variations
Summary
Biometrics has recently been widely used for human recognition in many different countries to identify a person under controlled or uncontrolled environments. Universality is one of the most important factors which means that every person should have that characteristic. Absence of the factors, such as universality, uniqueness, permanence, and acceptability, leads to a weak recognition system with high error rates. Universality, permanence, and acceptability are satisfied, but the factor that represents a serious problem is the uniqueness. It is axiomatic that the identical twins have the same face shape, size, and features, so new methods and algorithms should be studied and considered in order to deal with the high similarities in case of identical twins.
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