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Optimal feature selection-based biometric key management for identity management system: Emotion oriented facial biometric system
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2020-12-13 , DOI: 10.1016/j.jvcir.2020.103002
Suresh Padmanabhan , Radhika K.R.

Identity management systems with biometric key binding make digital transactions secure and reliable. A novel methodology is proposed to develop an intelligent key management system using facial emotions. Key binding with facial emotions makes use of an intrinsic user specific trait facilitating a more natural computer to human interaction. The proposed system utilizes metaheuristic swarm intelligence based optimization techniques to extract optimal features. The work demonstrates key binding by encrypting an image with a secret key bound to optimal features extracted from facial emotions. Efficiency and correctness of proposed key management is validated by successful decryption at receiving end with any one of the enrolled emotions given as input. Deer Hunting Optimization Algorithm and Chicken Swarm Optimization are merged to select optimal features from facial emotions. The derived algorithm is called Fitness Sorted Deer Hunting Optimization Algorithm with Rooster Update. Seven facial emotions — anger, disgust, fear, happiness, sadness, surprise and neutral are used to extract optimal features from Japanese Female Facial Expressions and Yale Facial datasets to train the neural network. Proposed work achieved better performance results over state-of-art optimization algorithms such as whale optimization algorithm, grey wolf optimization, chicken swarm optimization and deer hunting optimization algorithm. Accuracy of proposed model is 2.2% better than deer hunting optimization algorithm and 12.3% better than chicken swarm optimization for a key length 80.



中文翻译:

基于最优特征选择的身份管理系统生物特征密钥管理:面向情感的面部生物特征识别系统

具有生物特征识别键绑定的身份管理系统使数字交易安全可靠。提出了一种新颖的方法来开发利用面部表情的智能钥匙管理系统。与面部情感的按键绑定利用了特定于用户的固有特征,从而促进了更自然的计算机与人的互动。提出的系统利用基于元启发式群智能的优化技术来提取最佳特征。该作品通过用绑定到从面部表情中提取的最佳特征的秘密密钥加密图像来演示密钥绑定。提议的密钥管理的效率和正确性可以通过在接收端成功解密并将输入的任何一种情绪作为输入来验证。猎鹿优化算法和鸡群优化算法相结合,从面部表情中选择最佳特征。导出的算法称为“具有公鸡更新的适应度排序的鹿狩猎优化算法”。七个面部表情-愤怒,厌恶,恐惧,幸福,悲伤,惊奇和中立被用来从日本女性面部表情和耶鲁面部数据集中提取最佳特征,以训练神经网络。拟议的工作比最先进的优化算法(如鲸鱼优化算法,灰太狼优化,鸡群优化和猎鹿优化算法)取得了更好的性能结果。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。导出的算法称为“具有公鸡更新的适应度排序的鹿狩猎优化算法”。七个面部表情-愤怒,厌恶,恐惧,幸福,悲伤,惊奇和中立被用来从日本女性面部表情和耶鲁面部数据集中提取最佳特征,以训练神经网络。拟议的工作比最先进的优化算法(如鲸鱼优化算法,灰太狼优化,鸡群优化和猎鹿优化算法)获得了更好的性能结果。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。导出的算法称为“具有公鸡更新的适应度排序的鹿狩猎优化算法”。七个面部表情-愤怒,厌恶,恐惧,幸福,悲伤,惊奇和中立被用来从日本女性面部表情和耶鲁面部数据集中提取最佳特征,以训练神经网络。拟议的工作比最先进的优化算法(如鲸鱼优化算法,灰太狼优化,鸡群优化和猎鹿优化算法)取得了更好的性能结果。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。惊喜和中性用于从日本女性面部表情和耶鲁面部数据集中提取最佳特征,以训练神经网络。拟议的工作比最先进的优化算法(如鲸鱼优化算法,灰太狼优化,鸡群优化和猎鹿优化算法)取得了更好的性能结果。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。惊喜和中性用于从日本女性面部表情和耶鲁面部数据集中提取最佳特征,以训练神经网络。拟议的工作比最先进的优化算法(如鲸鱼优化算法,灰太狼优化,鸡群优化和猎鹿优化算法)取得了更好的性能结果。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。鸡群优化和猎鹿优化算法。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。鸡群优化和猎鹿优化算法。对于密钥长度80,所提出模型的精度比猎鹿优化算法高2.2%,比鸡群优化高12.3%。

更新日期:2020-12-17
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