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Although, biometrics provide high-confidence and trusted security, they suffer from a fatal weakness that emerges from permanence and limitation in quantities. Such a drawback puts biometric data under a substantial risk of fraudulent, which makes the replacement of traditional authentication systems infeasible with the lack of proper biometric data protection. This paper presents a novel biometric protection method to generate secure facial biometric templates used in statistical-based recognition algorithms such as 2DPCA. Original biometrics are polynomially transformed to the secure domain, where cooccurrence matrices are used to generate the final templates. The paper presents a unique relationship established by the Hadamard product within the transformation. The generated secure templates are used in the same fashion as original biometrics for evaluations using 2DPCA without any change to the recognition algorithm. And yet, evaluations confirm high security with enhanced recognition accuracy by 3% and 4.5% over the original and other transformed data respectively.