Patent Publication Number: US-2020293886-A1

Title: Authentication method and apparatus with transformation model

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit under 35 USC § 119(a) of Korean Patent Application No. 10-2019-0030147 filed on Mar. 15, 2019 in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes. 
     BACKGROUND 
     1. Field 
     The following description relates to an authentication method and apparatus with a transformation model. 
     2. Description of Related Art 
     Technological automation of speech recognition has been implemented through processor implemented neural network models, as specialized computational architectures, that after substantial training may provide computationally intuitive mappings between input patterns and output patterns. The trained capability of generating such mappings may be referred to as a learning capability of the neural network. Further, because of the specialized training, such specially trained neural network may thereby have a generalization that generates a relatively accurate output with respect to an input pattern that the neural network may not have been trained for, for example. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     In a general aspect, a processor implemented authentication method includes obtaining a second enrolled feature to which a first enrolled feature generated based on a first feature extractor is transformed; determining an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor; and performing an authentication based on the second enrolled feature and the input feature. 
     The obtaining may include transforming the first enrolled feature to the second enrolled feature with a transformation model. 
     The obtaining may include receiving, from another apparatus, the second enrolled feature to which the first enrolled feature is transformed. 
     The second feature extractor may be an updated version of the first feature extractor. 
     The second enrolled feature may be obtained based on a transformation model. 
     The transformation model may include a structural element that corresponds to a difference between a structure of the first feature extractor and a structure of the second feature extractor. 
     The first feature extractor may be pretrained to output first output data in response to an input of first input data, the second feature extractor is pretrained to output second output data in response to an input of the first input data, and the transformation model is pretrained to output the second output data in response to an input of the first output data. 
     The first enrolled feature may include first sub-enrolled features, and the second enrolled feature includes second sub-enrolled features to which the first sub-enrolled features are transformed. 
     The method may include discarding at least a portion of the second sub-enrolled features based on suitabilities of the second sub-enrolled features. 
     The method may include discarding at least a portion of the second sub-enrolled features based on a similarity between the second sub-enrolled features. 
     The discarding may include discarding at least one of the second sub-enrolled features based on a second threshold and similarities between the at least one second sub-enrolled feature and remaining second sub-enrolled features. 
     The performing may include performing the authentication based on a first threshold and a similarity between the second enrolled feature and the input feature, wherein the first threshold is equal to the second threshold. 
     In a general aspect, an authentication apparatus includes one or more processors configured to obtain a second enrolled feature to which a first enrolled feature is transformed; determine an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor; and perform an authentication based on the second enrolled feature and the input feature. 
     The second enrolled feature may be obtained based on a transformation model. 
     The transformation model may include a structural element that corresponds to a difference between a structure of the first feature extractor and a structure of the second feature extractor. 
     The first feature extractor may be pretrained to output first output data in response to an input of first input data, the second feature extractor is pretrained to output second output data in response to an input of the first input data, and the transformation model is pretrained to output the second output data in response to an input of the first output data. 
     The first enrolled feature may include first sub-enrolled features, and the second enrolled feature includes second sub-enrolled features to which the first sub-enrolled features are transformed. 
     The one or more processors may be configured to discard at least a portion of the second sub-enrolled features based on a similarity between the second sub-enrolled features. 
     The one or more processors may be configured to discard one of the second sub-enrolled features based on a threshold and similarities between the one second sub-enrolled feature and the remaining second sub-enrolled features. 
     The apparatus may further include a memory storing instructions that, when executed by the one or more processors, configure the one or more processors to perform the generating of the first enrolled feature, the obtaining of the second enrolled feature, the determining of the input feature, and the performing of the authentication. 
     Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates an example of an enrollment process and an authentication process in accordance with one or more embodiments. 
         FIG. 2  illustrates an example of an authentication process with a transformation model in accordance with one or more embodiments. 
         FIG. 3  illustrates an example of an operation of a transformation model in accordance with one or more embodiments. 
         FIG. 4  illustrates an example of generating a transformation model in accordance with one or more embodiments. 
         FIG. 5  illustrates an example of generating a transformation model in accordance with one or more embodiments. 
         FIG. 6  illustrates an example of second sub-enrolled features in accordance with one or more embodiments. 
         FIG. 7  illustrates an example of a transformation operation of a user terminal in accordance with one or more embodiments. 
         FIG. 8  illustrates an example of a transformation operation through a server in accordance with one or more embodiments. 
         FIG. 9  illustrates an example of a transformation operation through a hub device in an Internet of things (IoT) system in accordance with one or more embodiments. 
         FIG. 10  illustrates an example of a transformation operation through a hub device in an IoT system in accordance with one or more embodiments. 
         FIG. 11  illustrates an example of a configuration of an authentication apparatus in accordance with one or more embodiments. 
         FIG. 12  illustrates an example of an authentication method in accordance with one or more embodiments. 
     
    
    
     Throughout the drawings and the detailed description, unless otherwise described or provided, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The drawings may not be to scale, and the relative size, proportions, and depiction of elements in the drawings may be exaggerated for clarity, illustration, and convenience. 
     DETAILED DESCRIPTION 
     The following detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatuses, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be apparent after an understanding of the disclosure of this application. For example, the sequences of operations described herein are merely examples, and are not limited to those set forth herein, but may be changed as will be apparent after an understanding of the disclosure of this application, with the exception of operations necessarily occurring in a certain order. Also, descriptions of features that are known in the art may be omitted for increased clarity and conciseness. 
     The terminology used herein is for describing various examples only, and is not to be used to limit the disclosure. The articles “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms “comprises,” “includes,” and “has” specify the presence of stated features, numbers, operations, members, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, members, elements, and/or combinations thereof. 
     Throughout the specification, when an element, such as a layer, region, or substrate, is described as being “on,” “connected to,” or “coupled to” another element, it may be directly “on,” “connected to,” or “coupled to” the other element, or there may be one or more other elements intervening therebetween. In contrast, when an element is described as being “directly on,” “directly connected to,” or “directly coupled to” another element, there can be no other elements intervening therebetween. 
     As used herein, the term “and/or” includes any one and any combination of any two or more of the associated listed items. 
     Although terms such as “first,” “second,” and “third” may be used herein to describe various members, components, regions, layers, or sections, these members, components, regions, layers, or sections are not to be limited by these terms. Rather, these terms are only used to distinguish one member, component, region, layer, or section from another member, component, region, layer, or section. Thus, a first member, component, region, layer, or section referred to in examples described herein may also be referred to as a second member, component, region, layer, or section without departing from the teachings of the examples. 
     Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains after an understanding of the disclosure of this application. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the disclosure of the present application, and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
       FIG. 1  illustrates an example of an enrollment process and an authentication process, in accordance with one or more embodiments. 
     Referring to  FIG. 1 , an authentication apparatus  100  receives input data and generates output data by processing the received input data. The input data may correspond to an input voice or speech, or an input image, but is not limited thereto. For example, in an example of speech recognition, the input data may include voice or audio data. In an example of facial recognition, the input data may include a facial image. In an example of fingerprint recognition, the input data may include a fingerprint image. In an example of iris recognition, the input data may include an iris image. The output data may include an authentication result. For example, the authentication result may indicate an authentication success or an authentication failure. Herein, it is noted that use of the term ‘may’ with respect to an example or embodiment, e.g., as to what an example or embodiment may include or implement, means that at least one example or embodiment exists where such a feature is included or implemented while all examples and embodiments are not limited thereto. 
     Referring again to  FIG. 1 , the authentication apparatus  100  performs an enrollment process  151  and an authentication process  152 . The enrollment process  151  is a process where a legitimate user enrolls or registers himself or herself in the authentication apparatus  100 , and the authentication process  152  is a process in which a test user attempts an authentication by incorrectly claiming that he or she is a legitimate user. 
     In a non-limited example, the enrollment process  151  may be performed before the authentication process  152 . According to the enrollment process  151 , information pertaining to the legitimate user is enrolled or registered in the authentication apparatus  100 . The information of the legitimate user enrolled in the authentication apparatus  100  is referred to as enrolled information or an enrolled feature. It may be expressed that the enrolled information is “pre”enrolled. Here, “pre” means before the authentication process  152 . According to the authentication process  152 , an authentication is deemed to be successful when the test user corresponds to a legitimate user, and the authentication is deemed to fail when the test user does not correspond to a legitimate user. 
     In the enrollment process  151 , the authentication apparatus  100  extracts at least one feature from the input data using a feature extractor  110 , and creates an enrolled feature based on the extracted at least one feature. The authentication apparatus  100  stores the enrolled feature as information of the legitimate user. 
     In the authentication process  152 , the authentication apparatus  100  extracts at least one feature from the input data using the feature extractor  110 , and determines an input feature based on the extracted at least one feature. The authentication apparatus  100  compares the enrolled feature and the input feature using a comparator  120 , and generates an authentication result based on a comparison result. For example, the comparison result may include a similarity or a difference between the enrolled feature and the input feature, and the authentication apparatus  100  may generate the authentication result by comparing the similarity or the difference between the enrolled feature and the input feature, to a threshold, hereinafter identified as TH 1 . 
     The feature extractor  110  and the comparator  120  are each implemented through at least one hardware module, at least one software module, or a combination thereof. For example, the feature extractor  110  and the comparator  120  are each implemented as a neural network. In this example, at least a portion of the neural network is implemented as software, hardware including a neural processor, or a combination thereof. Technological automation of pattern recognition or analyses, for example, has been implemented through processor implemented neural network models, as specialized computational architectures, that after substantial training may provide computationally intuitive mappings between input patterns and output patterns or pattern recognitions of input patterns. The trained capability of generating such mappings or performing such pattern recognitions may be referred to as a learning capability of the neural network. Such trained capabilities may also enable the specialized computational architecture to classify such an input pattern, or portion of the input pattern, as a member that belongs to one or more predetermined groups. Further, because of the specialized training, such specially trained neural network may thereby have a generalization capability of generating a relatively accurate or reliable output with respect to an input pattern that the neural network may not have been trained for, for example. 
     In an example, the neural network may correspond to a deep neural network (DNN) including a fully connected network, a deep convolutional network, and a recurrent neural network, or may include different or overlapping neural network portions respectively with such full, convolutional, or recurrent connections, according to an algorithm used to process information. The DNN includes a plurality of layers. The plurality of layers includes an input layer, at least one hidden layer, and an output layer. For example, the DNN may include an input layer to which input data is applied, an output layer for outputting a result derived through prediction based on training and the input data, and a plurality of hidden layers for performing a neural network operation between the input layer and the output layer. 
     The neural network may be configured to perform as non-limiting examples, object classification, object recognition, voice recognition, and image recognition by mutually mapping input data and output data having a nonlinear relationship based on deep learning. Such deep learning is indicative of a processor implemented machine learning scheme for solving issues, such as issues related to automated image or speech recognition from a big data set, as non-limiting examples. Deep learning is construed as an optimization problem solving process of finding a point at which energy is minimized while training a neural network using prepared training data. Through supervised or unsupervised learning of deep learning, a structure of the neural network or a weight corresponding to a model is obtained, and the input data and the output data are mapped to each other through the weight. 
     The neural network is trained based on the training data in a training operation, and performs an inference operation such as classification, recognition, or detection related to the input data in an inference operation. It may be expressed that the neural network is “pre”trained. Here, “pre” means before input data for inference is input into the neural network. That the neural network “starts” means that the neural network is ready for inference. For example, that the neural network “starts” includes that the neural network is loaded into a memory, or that input data for inference is input into the neural network after the neural network is loaded into the memory. 
     The feature extractor  110  generates a feature vector by extracting at least one feature from the input data. The feature extractor  110  may include a neural network that is pretrained to extract features from a network input. The comparator  120  outputs a comparison result including a similarity or a difference between an input feature vector and an enrolled feature vector. The comparator  120  may include a neural network that is pretrained to output a result of the comparison between the input to the network and the enrolled feature vector. 
     In the event that a model change or an update related to the authentication apparatus  100  is made, the feature extractor  110  may be changed to another feature extractor. Hereinafter, the feature extractor  110  is referred to as an old feature extractor or a first feature extractor, and the changed feature extractor that is different from the feature extractor  110  is referred to as a new feature extractor or a second feature extractor. For example, the new feature extractor may be an updated version of the old feature extractor. The old feature extractor and the new feature extractor may extract features using different schemes. In this example, it may be difficult to perform the authentication process  152  using an enrolled feature generated through the old feature extractor and an input feature generated through the second feature extractor. 
     To perform the authentication process  152  based on the new feature extractor, an enrolled feature corresponding to the new feature extractor should be provided. However, the enrollment of a new enrolled feature using the new feature extractor may be inconvenient for users. 
     In an example, an enrolled feature corresponding to an old or previous enrolled feature extractor is transformed to an enrolled feature corresponding to the new or updated feature extractor based on a transformation model, and the authentication process  152  is performed based on the new enrolled feature. Hereinafter, the term “old enrolled feature” or “first enrolled feature” is used in relation to the old feature extractor or the first feature extractor, and the term “new enrolled feature” or “second enrolled feature” is used in relation to the new feature extractor or the second feature extractor and the transformation model. 
       FIG. 2  illustrates an example of an authentication process using a transformation model, in accordance with one or more embodiments. 
     Referring to  FIG. 2 , a transformation model  210  transforms a first enrolled feature to a second enrolled feature. The first enrolled feature refers to an old enrolled feature. The first enrolled feature may be a feature enrolled using the feature extractor  110  of  FIG. 1  through the enrollment process  151  of  FIG. 1 . In this example, the first enrolled feature is construed as corresponding to the feature extractor  110  of  FIG. 1 , and the second enrolled feature is construed as corresponding to a feature extractor  220  of  FIG. 2 . The feature extractor  220  is an updated version of the feature extractor  110  of  FIG. 1 . 
     An authentication apparatus  200  determines an input feature by extracting a feature from input data using the feature extractor  220 . For ease of description, the input feature generated based on the feature extractor  110  of  FIG. 1  is referred to as a first input feature, and the input feature generated based on the feature extractor  220  is referred to as a second input feature. The authentication apparatus  200  performs an authentication based on the second enrolled feature and the second input feature. For example, the authentication apparatus  200  determines a comparison result by comparing the second enrolled feature and the second input feature using a comparator  230 , and generates an authentication result based on the comparison result and a threshold. 
     The feature extractor  220  and the comparator  230  are each implemented through at least one hardware module, at least one software module, or a combination thereof. For example, the feature extractor  220  and the comparator  230  may each be implemented as a neural network. The description of the neural network of  FIG. 1  applies to the feature extractor  220  and the comparator  230  of  FIG. 2 . 
     In a non-limiting example transformation process  251  is performed by the authentication apparatus  200 . In another example, the transformation process  251  may be implemented by another apparatus different from the authentication apparatus  200 . For example, the authentication apparatus  200  includes the transformation model  210 , and transforms the first enrolled feature to the second enrolled feature using the transformation model  210 . In another example, instead of the authentication apparatus  200 , another apparatus may include the transformation model  210 . In this example, the other apparatus may transform the first enrolled feature to the second enrolled feature using the transformation model  210 , and the authentication apparatus  200  may then receive the second enrolled feature to which the first enrolled feature is transformed from the other apparatus. Thus, in an example, the authentication apparatus  200  may obtain the second enrolled feature from an external apparatus. 
       FIG. 3  illustrates an example of an operation of a transformation model in accordance with one or more embodiments. 
     Referring to  FIG. 3 , a transformation model  330  transforms a first enrolled feature corresponding to a first feature extractor  310  to a second enrolled feature corresponding to a second feature extractor  320 . The first feature extractor  310  and the second feature extractor  320  are different from each other. For example, the second feature extractor  320  may be a modification of the first feature extractor  310 . In detail, the second feature extractor  320  may be an updated version of the first feature extractor  310 . The transformation model  330  is generated based on the first feature extractor  310  and the second feature extractor  320 . 
     The transformation model  330  includes structural elements corresponding to differences between a structure of the first feature extractor  310  and a structure of the second feature extractor  320 , and transforms the first enrolled feature to the second enrolled feature using the corresponding structural elements. In another example, the transformation model  330  is pretrained to output an output of the second feature extractor  320  when an output of the first feature extractor  310  is input thereinto. Through the training as described above, the transformation model  330  outputs the second enrolled feature in response to an input of the first enrolled feature. 
       FIG. 4  illustrates an example of generating a transformation model. Referring to  FIG. 4 , a feature extractor  410  includes a plurality of layers  415 , and a feature extractor  420  includes the plurality of layers  415  and a plurality of layers  425 . In an example, the plurality of layers  415  and  425  correspond to convolutional layers. The feature extractor  410  corresponds to a first feature extractor, and the feature extractor  420  corresponds to a second feature extractor. 
     For example, the plurality of layers  425  is added to the second feature extractor  420  during a process of updating the first feature extractor  410  to the second feature extractor  420 . Structural elements corresponding to differences between a structure of the first feature extractor  410  and a structure of the second feature extractor  420  are the plurality of layers  425 . Thus, a transformation model  430  is generated based on the plurality of layers  425 . For example, the transformation model  430  is generated to include the plurality of layers  425 . 
     The first feature extractor  410  outputs first output data in response to an input of input data, and the second feature extractor  420  outputs second output data in response to an input of the input data. The transformation model  430  outputs the second output data in response to an input of the first output data. The first output data is output based on the plurality of layers  415 , and the second output data is output based on a combination of the plurality of layers  415  and the plurality of layers  425 . Thus, the first output data is transformed to the second output data through the plurality of layers  425  of the transformation model  430 . 
     The first output data and the second output data correspond to the first enrolled feature and the second enrolled feature of  FIG. 3 . Thus, the transformation model  430  transforms the first enrolled feature to the second enrolled feature based on the structural elements corresponding to the differences between the structure of the first feature extractor  410  and the structure of the second feature extractor  420 . 
       FIG. 5  illustrates an example of generating a transformation model. Referring to  FIG. 5 , a transformation model  530  is trained based on respective outputs of a feature extractor  510  and a feature extractor  520  with respect to the same input. The feature extractor  510  corresponds to a first feature extractor, and the feature extractor  520  corresponds to a second feature extractor. For example, the first feature extractor  510  outputs first output data in response to an input of input data, and the second feature extractor  520  outputs second output data in response to an input of the same input data. The transformation model  530  is trained to output the second output data in response to an input of the first output data. The first output data and the second output data correspond to the first enrolled feature and the second enrolled feature of  FIG. 3 . Thus, the transformation model  530  transforms the first enrolled feature to the second enrolled feature based on training as described above. 
       FIG. 6  illustrates an example of second sub-enrolled features in accordance with one or more embodiments. An enrolled feature includes sub-enrolled features. The sub-enrolled features may be determined through a single enrollment process, or may be determined through a plurality of enrollment processes. For example, in an example of face recognition, sub-enrolled features corresponding to respective parts of a face may be determined based on a single facial image or a plurality of facial images captured in an enrollment process. 
     A first enrolled feature includes first sub-enrolled features, and an authentication apparatus transforms the first sub-enrolled features to second sub-enrolled features using a transformation model. The second sub-enrolled features constitute a second enrolled feature. The authentication apparatus determines whether the second sub-enrolled features include an inappropriate sub-enrolled feature in order to prevent a false acceptance. The false acceptance may include the incorrect recognition of a test user who is not a legitimate user as being a legitimate user. The authentication apparatus determines suitabilities or similarities of the second sub-enrolled features to the first sub-enrolled features, or similarities between one second sub-enrolled feature and the remaining second sub-enrolled features, and discards at least a portion of the second sub-enrolled features based on the determined suitabilities or similarities of the second sub-enrolled features to the first sub-enrolled features, or determined similarities between one second sub-enrolled feature and the remaining second sub-enrolled features. 
     In  FIG. 6 , the indicated points are vectors corresponding to the second sub-enrolled features represented on a predetermined coordinate plane. The second sub-enrolled features are classified as second sub-enrolled features  610  forming a cluster and as second sub-enrolled features  620  and  630  corresponding to outliers. The authentication apparatus calculates suitabilities or similarities of the second sub-enrolled features  620  and  630  corresponding to the outliers to be relatively low, and configures a second enrolled feature by excluding the second sub-enrolled features  620  and  630 . 
     The authentication apparatus discards at least a portion of the second sub-enrolled features based on a similarity of the second sub-enrolled features. For example, the authentication apparatus discards one of the second sub-enrolled features based on a threshold, hereinafter, identified as TH 2 , and similarities between the one second sub-enrolled feature and the remaining second sub-enrolled features. 
     A similarity parameter related to each second sub-enrolled feature is determined based on Equation 1 below. 
     
       
         
           
             
               
                 
                   
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     In Equation 1, T(k) denotes a similarity parameter related to a second sub-enrolled feature corresponding to an index k, N denotes a total number of second sub-enrolled features, and Similarity(b k , b j ) denotes a similarity between a second sub-enrolled feature b k  and a second sub-enrolled feature b j . For example, Similarity(b k , b j ) is determined based on a distance or a difference between the second sub-enrolled feature b k  and the second sub-enrolled feature b j . Similarity(b k , b j ) has a relatively large value as the distance between the second sub-enrolled feature b k  and the second sub-enrolled feature b j  decreases. A sum of Similarity(b k , b j ) values is divided by N−1, and thus the similarity parameter is also referred to as a normalized similarity. 
     The authentication apparatus determines the similarity parameter related to each second sub-enrolled feature based on Equation 1, and discards at least a portion of the second sub-enrolled features based on the threshold TH 2  and the similarity parameter. For example, the authentication apparatus excludes a second sub-enrolled feature having a similarity parameter less than the threshold TH 2 . 
     In an example, the threshold TH 2  is equal to a threshold, hereinafter, identified as TH 1 , used in an authentication process. For example, the authentication apparatus performs an authentication based on the threshold TH 1  and a similarity between a second enrolled feature and a second input feature. The second enrolled feature is an enrolled feature to which the first enrolled feature is transformed by a transformation model, and the second input feature is extracted from input data using a second feature extractor. However, examples are not limited thereto. The threshold TH 2  may have various values different from the threshold TH 1 . 
     A transformation process is performed by various devices.  FIGS. 7 through 10  illustrate various examples of a transformation operation. A transformation of an enrolled feature may also be implemented through various examples other than the examples set forth hereinafter with reference to  FIGS. 7 through 10 . A user terminal  700  of  FIG. 7 , a user terminal  820  of  FIG. 8 , Internet of things (IoT) terminals  920 ,  930 , and  940  of  FIG. 9 , and IoT terminals  1020 , 1030 , and  1040  of  FIG. 10  each include the authentication apparatus described with reference to  FIGS. 1 through 6 , or perform the functions of the authentication apparatus described with reference to  FIGS. 1 through 6 . 
       FIG. 7  illustrates an example of a transformation operation of a user terminal in accordance with one or more embodiments. 
     Referring to  FIG. 7 , the user terminal  700  includes a transformation model  750 . The user terminal  700  transforms a first enrolled feature to a second enrolled feature using the transformation model  750 . The transformation model  750  may be generated by the user terminal  700 , or may be generated by another apparatus different from the user terminal  700 , for example, a server. The user terminal  700  performs an authentication process based on the second enrolled feature. 
       FIG. 8  illustrates an example of a transformation operation that is performed through a server in accordance with one or more embodiments. 
     Referring to  FIG. 8 , a server  810  includes a transformation model  815 , and the user terminal  820  transforms a first enrolled feature to a second enrolled feature using the transformation model  815  in the server  810 . For example, the user terminal  820  transmits the first enrolled feature to the server  810 , and the server  810  transforms the first enrolled feature to the second enrolled feature using the transformation model  815  and transmits the second enrolled feature to the user terminal  820 . The transformation model  815  is generated by the server  810 . The user terminal  820  performs an authentication process based on the second enrolled feature. 
       FIG. 9  illustrates an example of a transformation operation that is performed through a hub device in an IoT system in accordance with one or more embodiments. 
     Referring to  FIG. 9 , a hub device  910  includes a transformation model  915 , and the IoT terminals  920 ,  930 , and  940 . The IoT terminals  920 ,  930 , and  940  each transform an old enrolled feature to a new enrolled feature using the transformation model  915  in the hub device  910 . For example, the IoT terminals  920 ,  930 , and  940  transmit the old enrolled features to the hub device  910 , and the hub device  910  transforms the old enrolled features to new enrolled features using the transformation model  915  and transmits the new enrolled features to the respective IoT terminals  920 ,  930 , and  940 . The transformation model  915  may be generated by the hub device  910 , or may be generated by another apparatus different from the hub device  910 , for example, a server. The IoT terminals  920 ,  930 , and  940  may each perform an authentication process based on the corresponding new enrolled feature. 
       FIG. 10  illustrates an example of a transformation operation through a hub device in an IoT system in accordance with one or more embodiments. 
     Referring to  FIG. 10 , a hub device  1010  includes a transformation model  1015 . The transformation model  1015  includes, as non-limited examples, a first sub-transformation model  1016 , a second sub-transformation model  1017 , and a third sub-transformation model  1018 . The first sub-transformation model  1016  transforms a first enrolled feature to a second enrolled feature, the second sub-transformation model  1017  transforms the first enrolled feature to a third enrolled feature, and the third sub-transformation model  1018  transforms the first enrolled feature to a fourth enrolled feature. 
     The first enrolled feature corresponds to a first feature extractor used by the hub device  1010 , the second enrolled feature corresponds to a second feature extractor used by the IoT terminal  1020 , the third enrolled feature corresponds to a third feature extractor used by the IoT terminal  1030 , and the fourth enrolled feature corresponds to a fourth feature extractor used by the IoT terminal  1040 . In an example, the first feature extractor may also be used by another representation device of the IoT system. The first sub-transformation model  1016  is generated based on the first feature extractor and the second feature extractor, the second sub-transformation model  1017  is generated based on the first feature extractor and the third feature extractor, and the third sub-transformation model  1018  is generated based on the first feature extractor and the fourth feature extractor. Contrary to the definitions provided above, in  FIG. 10 , the first enrolled feature is referred to as a source enrolled feature which is a feature enrolled through the hub device  1010  or a representative device, and the second enrolled feature, the third enrolled feature, and the fourth enrolled feature are referred to as individual enrolled features which are enrolled features used respectively by the IoT terminals  1020 ,  1030 , and  1040 . 
     The hub device  1010  separately transforms the first enrolled feature to the second enrolled feature, the third enrolled feature, and the fourth enrolled feature using the transformation model  1015 , and respectively transmits the second enrolled feature, the third enrolled feature, and the fourth enrolled feature to the respective IoT terminals  1010 ,  1020 , and  1030 . The transformation model  1015  may be generated by the hub device  1010 , or may be generated by another apparatus different from the hub device  1010 , for example, a server. The IoT terminals  1010 ,  1020 , and  1030  perform authentication processes based on the second enrolled feature, the third enrolled feature, and the fourth enrolled feature. 
       FIG. 11  illustrates an example of a configuration of an authentication apparatus in accordance with one or more embodiments. 
     Referring to  FIG. 11 , an authentication apparatus  1100  includes a processor  1110  and a memory  1120 . In an example, the authentication apparatus  1100  may further store instructions, e.g., in memory  1120 , which when executed by the processor  1110  configure the processor  1110  to implement one or more or any combination of operations herein. The processor  1110  and the memory  1120  may be respectively representative of one or more processors  1110  and one or more memories  120 . 
     The authentication apparatus  1100  performs the at least one operation described or illustrated herein in relation to an authentication, and provides an authentication result to a user. The memory  1120  is connected to the processor  1110 , and stores instructions executable by the processor  1110 , data to be calculated by the processor  1110 , or data processed by the processor  1110 . The memory  1120  includes a non-transitory computer readable medium, for example, a high-speed random access memory, and/or a non-volatile computer readable storage medium, for example, at least one disk storage device, flash memory device, or other non-volatile solid state memory devices. 
     The processor  1110  executes instructions to perform the at least one operation described with reference to  FIGS. 1 through 10 . For example, the processor  1110  obtains a second enrolled feature to which a first enrolled feature generated based on a first feature extractor is transformed, determines an input feature by extracting a feature from input data using a second feature extractor different from the first feature extractor, and performs an authentication based on the second enrolled feature and the input feature. 
       FIG. 12  illustrates an example of an authentication method in accordance with one or more embodiments. The operations in  FIG. 12  may be performed in the sequence and manner as shown, although the order of some operations may be changed or some of the operations omitted without departing from the spirit and scope of the illustrative examples described. Many of the operations shown in  FIG. 12  may be performed in parallel or concurrently. One or more blocks of  FIG. 12 , and combinations of the blocks, can be implemented by special purpose hardware-based computer that perform the specified functions, or combinations of special purpose hardware and computer instructions. In addition to the description of  FIG. 12  below, the descriptions of  FIGS. 1-11  are also applicable to  FIG. 12 , and are incorporated herein by reference. Thus, the above description may not be repeated here. 
     Referring to  FIG. 12 , in operation  1210 , an authentication apparatus obtains a second enrolled feature to which a first enrolled feature generated based on a first feature extractor is transformed. In operation  1220 , the authentication apparatus determines an input feature by extracting a feature from input data using a second feature extractor different from the first feature extractor. In operation  1230 , the authentication apparatus discards at least a portion of second sub-enrolled features included in the second enrolled feature based on suitabilities or similarities of the second sub-enrolled features. In operation  1240 , the authentication apparatus performs an authentication based on the second enrolled feature and the input feature. In addition, the description provided with reference to  FIGS. 1 through 10  applies to the authentication method. 
     The authentication apparatuses  100 ,  200 , and  1100 , the feature extractor  110 , the comparator  120 , transformation model  210 , feature extractor  220 , comparator  230 , first extractor  310 , second extractor  320 , transformation model  330 , feature extractor  410 , feature  420 , transformation model  430 , first feature extractor  510 , second feature extractor  520 , transformation model  530 , user terminal  700 , transformation model  750 , server  810 , transformation model  15 , user terminal  820 , hub device  910 , transformation  915 , IoT terminals  920 ,  930 , and  940 , hub device  1010 , IoT terminals  1029 ,  1030 , and  1040 , authentication apparatus  1100 , processor  1110 , and memory  1120 , and other apparatuses, units, modules, devices, and other components described herein with respect to  FIGS. 1-12  are implemented as and by hardware components. Examples of hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer. Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application. The hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software. For simplicity, the singular term “processor” or “computer” may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. A hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, and multiple-instruction multiple-data (MIMD) multiprocessing. 
     The methods illustrated in  FIGS. 1-12  that perform the operations described in this application are performed by computing hardware, for example, by one or more processors or computers, implemented as described above executing instructions or software to perform the operations described in this application that are performed by the methods. For example, a single operation or two or more operations may be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations may be performed by one or more processors, or a processor and a controller, and one or more other operations may be performed by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may perform a single operation, or two or more operations. 
     Instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above are written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the processor or computer to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods as described above. In one example, the instructions or software include machine code that is directly executed by the processor or computer, such as machine code produced by a compiler. In another example, the instructions or software include higher-level code that is executed by the processor or computer using an interpreter. Programmers of ordinary skill in the art can readily write the instructions or software based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods as described above. 
     The instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, are recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media. Examples of a non-transitory computer-readable storage medium include read-only memory (ROM), random-access programmable read only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, a card type memory such as multimedia card micro or a card (for example, secure digital (SD) or extreme digital (XD)), magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device that is configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and providing the instructions or software and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the instructions. 
     While this disclosure includes specific examples, it will be apparent to one of ordinary skill in the art that various changes in form and details may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered in a descriptive sense only, and not for purposes of limitation. Descriptions of features or aspects in each example are to be considered as being applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order, and/or if components in a described system, architecture, device, or circuit are combined in a different manner, and/or replaced or supplemented by other components or their equivalents. Therefore, the scope of the disclosure is defined not by the detailed description, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents are to be construed as being included in the disclosure.