Patent Publication Number: US-2022237405-A1

Title: Data recognition apparatus and recognition method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of U.S. provisional application Ser. No. 63/142,980, filed on Jan. 28, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
    
    
     BACKGROUND 
     Technical Field 
     The disclosure relates to a data recognition apparatus and a recognition method thereof, and in particular, relates to a data recognition apparatus and a recognition method thereof capable of improving recognition rates. 
     Description of Related Art 
     At present, it is common to apply artificial intelligence to data recognition in the technical field. 
     In the related art, a memory is used most of the time to record multiple target information. When recognition occurs, the searched information is compared with the target information to look up the relevant data of the searched information. Nevertheless, the recognition rate of this approach is often limited by the volume of target information. Generally, with a limited volume of target information, the recognition rate of the data recognition apparatus is also limited. 
     SUMMARY 
     The disclosure provides a data recognition apparatus and a recognition method thereof capable of improving recognition rates. 
     The disclosure provides a data recognition apparatus including a data augmentation device, a feature extractor, and a comparator. The data augmentation device receives a plurality of target information and performs augmentation on each of the target information to generate a plurality of augmented target information. The feature extractor is coupled to the data augmentation device. The feature extractor receives queried information and the augmented target information to extract features of the augmented target information and the queried information to respectively generate a plurality of augmented target feature values and a queried feature value. The comparator generates a recognition result according to the queried feature value and the augmented target feature values. 
     The disclosure further provides a data recognition method including the following steps. A plurality of target information are received, and augmentation is performed on each of the target information to generate a plurality of augmented target information. Queried information and the augmented target information are received to extract features of the augmented target information and the queried information to respectively generate a plurality of augmented target feature values and a queried feature value. A recognition result is generated according to the queried feature value and the augmented target feature value. 
     To sum up, the data recognition apparatus provided by the disclosure generates multiple augmented target information through augmentation performed on each of the target information through the data augmentation device. The data recognition apparatus generates the recognition result according to the feature values of the augmented target information and the feature value of the queried information. The data recognition apparatus may be implemented as a memory. Based on the augmented target information, in the data recognition apparatus provided by the disclosure, recognition errors that may be caused by the error bits in the memory may be effectively lowered. Further, recognition errors that may occur between systems due to noise may be reduced, and accuracy rates of recognition are effectively improved. 
     To make the aforementioned more comprehensible, several embodiments accompanied with drawings are described in detail as follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure. 
         FIG. 1  is a schematic diagram illustrating a data recognition apparatus according to an embodiment of the disclosure. 
         FIG. 2  is a schematic diagram illustrating generation of augmented target information in the data recognition apparatus according to an embodiment of the disclosure. 
         FIG. 3  is a schematic diagram illustrating implementation of a feature extractor according to an embodiment of the disclosure. 
         FIG. 4A  and  FIG. 4B  are graphs illustrating relationships between recognition accuracy and bit resolution of the data recognition apparatus according to an embodiment of the disclosure. 
         FIG. 5  is a flow chart illustrating a data recognition method according to an embodiment of the disclosure. 
         FIG. 6  is a flow chart illustrating a data recognition method according to another embodiment of the disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     With reference to  FIG. 1 ,  FIG. 1  is a schematic diagram illustrating a data recognition apparatus according to an embodiment of the disclosure. A data recognition apparatus  100  includes a data augmentation device  110 , a feature extractor  120 , and a comparator  130 . The data augmentation device  110  is configured to receive a plurality of target information TI 1  to TI 3 . The data augmentation device  110  performs augmentation on each of the target information TI 1  to TI 3  to generate a plurality of augmented target information. The feature extractor  120  is coupled to the data augmentation device  110 . The feature extractor  120  receives the augmented target information generated by the data augmentation device  110  and generates a plurality of augmented target feature values TPF 1  to TPF 3  through extracting features of the augmented target information. Further, the feature extractor  120  receives queried information QI and extracts a feature of the queried information QI to generate a queried feature value QF. The comparator  130  is coupled to the feature extractor  120 . The comparator  130  compares the queried feature value QF with the augmented target feature values TPF 1  to TPF 3  and generates a recognition result according to recognition of similarity between the queried feature value QF and the augmented target feature values TPF 1  to TPF 3 . 
     In this embodiment, the data augmentation device  110  may perform augmentation on each of the target information TI 1  to TI 3  through a plurality of manners. Herein, taking the target information TI 1  to TI 3  acting as image information as an example, the data augmentation device  110  may geometrically adjust each of the target information TI 1  to TI 3  to generate the augmented target information. In detail, the data augmentation device  110  may set each of the target information TI 1  to TI 3  to generate positional shifting or rotating or to generate shifting and rotating at the same time to generate the augmented target information.  FIG. 2  is a schematic diagram illustrating generation of the augmented target information in the data recognition apparatus according to an embodiment of the disclosure. In  FIG. 2 , the data augmentation device  110  may set the target information TI 1  to rotate to generate augmented target information TPI 1 . Herein, the data augmentation device  110  may set the target information TI 1  to rotate at different angles to generate a plurality of augmented target information. Further, the data augmentation device  110  may also set the target information TI 1  to shift to generate augmented target information TPIN. Herein, the data augmentation device  110  may set the target information TI 1  to generate shifting of different degrees in different directions to generate a plurality of augmented target information. Besides, the data augmentation device  110  may also set the target information TI 1  to rotate and to shift to generate the augmented target information. 
     In this embodiment, the augmented target information TPI 1  to TPIN may be stored in a memory  210 . The memory  210  may be a volatile memory or a non-volatile memory, which is not particularly limited. 
     In addition to shifting and rotating, the data augmentation device  110  may also set each of the target information TI 1  to TI 3  to generate shear deformation, set each of the target information TI 1  to TI 3  to generate flipping in a vertical direction and/or a horizontal direction, perform image cropping on each of the target information TI 1  to TI 3 , perform image cropping-and-padding on each of the target information TI 1  to TI 3 , perform perspective transforming on each of the target information TI 1  to TI 3 , or perform elastic transforming on each of the target information TI 1  to TI 3  to generate the augmented target information TPI 1  to TPIN. 
     In addition, in this embodiment, the data augmentation device  110  may also adjust a color of each of the target information TI 1  to TI 3  to generate the augmented target information. In detail, the data augmentation device  110  may also perform color sharpening, perform brightness adjustment, perform gamma-contrasting, or perform color inverting on each of the target information TI 1  to TI 3  to generate the augmented target information. In this embodiment, the data augmentation device  110  may further generate the augmented target information according to a generative adversarial model (GAM) for each of the target information TI 1  to TI 3 . Herein, through the GAM, the data augmentation device  110  may add noise to each of the target information TI 1  to TI 3 , obscure each of the target information TI 1  to TI 3 , apply a transfer function to the X or Y axis (translate X or translate Y) of each target information TI 1  to TI 3 , apply a coarse-salt effect to each of the target information TI 1  to TI 3 , apply a super pixel effect to each of the target information TI 1  to TI 3 , or apply an embossing effect to each of the target information TI 1  to  113  to generate the augmented target information TPI 1  to TPIN. 
     Besides, the data augmentation device  110  may also generate a thick fog effect or add special effects of weather patterns such as clouds and snow on each of the target information TI 1  to  113  to generate the augmented target information TPI 1  to TPIN. 
     In this embodiment, a data volume of the augmented target information TPI 1  to TPIN may be 2 to 8 times a data volume of the target information TI 1  to TI 3 . 
     Based on the above, since the memory  210  stores multiple groups of the augmented target information TPI 1  to TPIN, the noise on the augmented target information TPI 1  to TPIN is not required to be excessively attended to, and robustness to noise is provided. As such, the memory  210  does not have to check an error correcting code (ECC) of the read data, and the working speed of the system may thus be effectively improved. 
     Incidentally, when acting as a volatile memory, the memory  210  may be a static random-access memory (SRAM), a dynamic random-access memory (DRAM), a resistive random-access memory (ReRAM), a magnetoresistive random-access memory (MRAM), or a ferroelectric field-effect transistor (FeFET) memory. When acting as a non-volatile memory, the memory  210  may be a flash memory of any type. 
     In addition, the comparator  130  provided by the embodiments of the disclosure may be implemented as a processor with computing capability (e.g., a central processing unit (CPU), may be implemented as an application specific integrated circuit (ASIC), or may be implemented as an in-memory computation device. Taking the implementation by an in-memory computation device as an example, the in-memory computation device may store the augmented target feature values TPF 1  to TPF 3  to be multiplied and accumulated together with the queried feature value QF, so as to recognize the similarity between the augmented target feature values TPF 1  to TPF 3  and the queried feature value QF to accordingly generate the recognition result. 
     In an embodiment of the disclosure, the comparator  130  may be configured to perform a Hamming distance calculation, a cosine distance calculation, or an Euclidean distance calculation to calculate the similarity between the queried feature value QF and the augmented target feature values TPF 1  to TPF 3 . 
     Herein, in this embodiment, the feature extractor  120  may be implemented by operations of an artificial neural network. The feature extractor  120  may also be implemented as a processor with computing capability (e.g., a CPU), may be implemented as an ASIC, or may be implemented as an in-memory computation device. An architecture of the artificial neural network in the feature extractor  120  may be determined by a designer and is not particularly limited. 
     The data augmentation device  110  in this embodiment may be implemented as a processor with computing capability (e.g., a CPU) or may be implemented as an ASIC, and implementation thereof is not particularly limited. 
     Taking a data recognition apparatus used in a company&#39;s security management system as an example, the data recognition apparatus  100  may be used to recognize whether a person entering or leaving the company is an employee of the company. A user may create multiple target information for all employees of the company. When the data recognition apparatus  100  is applied, the queried information may be compared with the target information, so as to learn whether the person corresponding to the queried information is an employee of the company and the person&#39;s access authority, so that the order of entering and leaving the company is effectively maintained. 
     With reference to  FIG. 3 ,  FIG. 3  is a schematic diagram illustrating implementation of a feature extractor according to an embodiment of the disclosure. A feature extractor  320  may be implemented by applying an artificial neural network operation. Herein, the feature extractor  320  may receive a plurality of sample information  310  and perform pre-training based on the sample information  310  to create nodes in an artificial neural network and a plurality of weight values. The feature extractor  320  may be a processor with computing capability, an ASIC, or an in-memory computation device. 
     The trained feature extractor  320  may be configured to execute features of the augmented target information and the queried information, and since related details are provided in the foregoing embodiments, description thereof is not repeated herein. 
     With reference to  FIG. 4A  and  FIG. 4B ,  FIG. 4A  and  FIG. 4B  are graphs illustrating relationships between recognition accuracy and bit resolution of the data recognition apparatus according to an embodiment of the disclosure. In  FIG. 4A , the points marked with X are recognition accuracy rates generated by the data recognition apparatus without adding the augmented target information. Herein, when the points correspond to the same bit resolution and the augmented target information is not added, the recognition accuracy rate generate by the data recognition apparatus is the lowest. Marks A 11  to A 18  refer to the recognition accuracy rates corresponding to different bit resolutions when the augmented target information, which is 3 times the target information, is added. Marks A 21  to A 28  refer to the recognition accuracy rates corresponding to different bit resolutions when the augmented target information, which is 2 times the target information, is added. Marks A 31  to A 38  refer to the recognition accuracy rates corresponding to different bit resolutions when the augmented target information, which is 1 time the target information, is added. It can be seen in  FIG. 4A  that when moderate augmented target information is added, the recognition accuracy rates may be effectively increased. 
     In addition, in  FIG. 4B , marks B 11  to B 18  are recognition correctness rates generated by the data recognition apparatus corresponding to different bit resolutions when there is no error bit when the augmented target information is stored. Marks B 21  to B 28  are the recognition correctness rates generated by the data recognition apparatus corresponding to different bit resolutions when 5% of the error bits occur when the augmented target information is stored. Marks B 31  to B 38  are the recognition correctness rates generated by the data recognition apparatus corresponding to different bit resolutions when 1% of the error bits occur when the augmented target information is stored. In can be seen in  FIG. 4B  that in the case that the augmented target information is added, the ratio of error bits generated by the memory does not have a significant impact on the recognition correctness of the data recognition apparatus. 
     With reference to  FIG. 5 ,  FIG. 5  is a flow chart illustrating a data recognition method according to an embodiment of the disclosure. Herein, in step S 510 , a plurality of target information are received, and augmentation is performed on each of the target information to generate a plurality of augmented target information. Next, in step S 520 , queried information and the augmented target information are received to extract features of the augmented target information and the queried information to respectively generate a plurality of augmented target feature values and a queried feature value. Finally, in step S 530 , similarity between the queried feature value and the augmented target feature values is recognized to generate a recognition result. 
     Implementation details of the steps in this embodiment are described in the foregoing embodiments in detail, and description thereof is thus not repeated herein. 
     With reference to  FIG. 6 ,  FIG. 6  is a flow chart illustrating a data recognition method according to another embodiment of the disclosure. Recognition of a user image is treated as an example in the embodiment of  FIG. 6 . In step S 610 , a user image (target image) is inputted to establish a database for recognition. Next, in step S 620 , augmentation is performed on the target information to generate a plurality of augmented target information. In step S 630 , the augmented target information is provided to a pre-trained model. Herein, the pre-trained model may be a feature extractor. In step S 640 , the augmented target information is stored in a memory. Finally, in step S 650 , recognition is performed through calculating similarity between queried information and the augmented target information. 
     In view of the foregoing, the data recognition apparatus provided by the disclosure generates multiple augmented target information through augmentation performed on the target information. Further, the feature value of the queried information and the feature values of the augmented target information are compared. As such, the recognition result is obtained through looking up the similarity between the feature value of the queried information and the feature values of the augmented target information. The augmented target information provided by the disclosure exhibits high robustness to noise, so that a decrease in the recognition rate of the system as affected by noise may be prevented. In addition, a memory may be applied for implementation of the data recognition apparatus in the embodiments of the disclosure. Based on the improved robustness of the augmented target information, the ECC-free memory may be used to increase the computing speed of the data recognition apparatus. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments without departing from the scope or spirit of the disclosure. In view of the foregoing, it is intended that the disclosure covers modifications and variations provided that they fall within the scope of the following claims and their equivalents.