# A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map
### Analysis of Common Identity Authentication Methods  
Broadly, biometric systems can be divided into two main types: unimodal and multimodal biometric systems. Unimodal systems are based on using a single source of information to establish the person's identity, for example, iris, retina, face, speech, fingerprint, finger-vein, signature, etc. The former six methods are based on biometrics, i.e., the use of various biological characteristics of the human body to achieve identity authentication. The last method, i.e., signature recognition, is a kind of behavior authentication method [1]. Many methods have been presented for identification and verification based on any of the above information sources, where every method employs a different strategy. A review of some prominent solutions is now presented. [PERSON] et al. in [2] proposed an efficient method for personal identification by analyzing iris patterns, which have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, they presented a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method, utilizing a weight vector initialization and winner selection. A method for human identification based on retinal images is presented in [3]. The proposed system is composed of two main parts: a feature-extraction component and a decision-making component. In the feature-extraction component, blood vessels are first extracted and then thinned by a morphological algorithm. Then, two feature vectors are constructed for each image, by utilizing angular and radial partitioning. In the article, a fuzzy system with Manhattan distances of two feature vectors as the input and a similarity measure as the output was added to the decision-making component. An exhaustive multifactor face authentication system using a neuro-fuzzy approach is described in [4]. During the enrollment process, facial region of a still image of the authorized user is captured and features are extracted using a local tetra pattern (LTrP) technique. The features are given as the input to the neural network, namely a fuzzy adaptive learning control network (FALCON), for training and classification of features. During the authentication process, an image that can vary with expression, pose, illumination and occlusion factors is taken as a test image and the test image is inputted to the LTrP and FALCON to train the system based on the features of the test image. Then, these trained features are compared with an existing feature set using the newly proposed multifactor face authentication algorithm to authenticate a person. [PERSON] and [PERSON], in [5], develop a voice-based authentication model that learns and discriminates each user's voice data using a deep neural network. In addition, they also present a synthesis speech detection method that is used to prevent a masquerading attack using synthetic voices. The proposed method can be divided into two modules: an MFCC based user authentication module and a Mel-Spectrogram-based synthetic speech detection module. A method for signature verification and recognition is presented in [6], which represents signature verification and recognition using zone wise statistical features. During the first phase, a knowledge base is constructed by training samples using the zone wise statistical features. During the second stage, i.e., the testing phase, the processed image is obtained with zoning wise statistical features and the signature is recognized using neural network classifiers. MATLAB was used to design this signature recognition and verification system. [PERSON] et al., in [7], present a new approach based on a deep learning model to achieve personal identification through finger vein patterns. They employed an improved deep network, named Merge Convolutional Neural Network (Merge CNN), which usesseveral CNNs with short paths.