Patent Description:
A user recognition technology may be used extensively for a surveillance and access control system requiring security and a smart device for providing customized services to a user, as a technology of recognizing a user by using registered user information. A user recognition device may recognize the user via a device such as an identification card, a key, or a smart card or may recognize the user via biometric recognition using a fingerprint, an iris, or the like.

An image-based user recognition device may obtain information of a face, a hand, an action, or the like of the user included in an image and may recognize the user on image captured to recognize the user, using the obtained information.

"<NPL> relates to a method for building a contour based face recognition process system so that it gives a user friendly environment to recognize a face or image.

An image-based user recognition device may recognize a user using a red green blue (RGB) image obtained via a frame-based vision sensor. The user recognition device may extract the features of a plurality of layers included in the whole region or a specific region of an RGB image to recognize the specific user. When a user is recognized using an RGB image, the user's privacy may be infringed by extracting the features of body such as the user's face, body, or the like. The privacy may be infringed when an RGB image including the user is transmitted to a server for accurate recognition. In the meantime, when the user recognition device recognizes the user by using an image of the shape (or contour) of an object obtained via an event-based vision sensor, the privacy problem may be solved, but it may be difficult to accurately recognize the user.

Accordingly, an aspect of the disclosure is to provide a user recognition device according to various embodiments of the disclosure that may generate an image including the shape of an object to train the shape of the user of the generated image, and thus may accurately recognize the user.

In accordance with an aspect of the disclosure, an electronic apparatus according to claim <NUM> is provided.

In accordance with another aspect of the disclosure, a control method according to claim <NUM> is provided.

According to various embodiments of the disclosure, a user recognition device may train the shape of a specified user, using an image including only the shape of an object, may generate shape information for recognizing a specified user based on the trained information, and may recognize the specified user based on the generated shape information, thereby correctly recognizing the specified user without any problem of privacy.

Besides, a variety of effects directly or indirectly understood through this disclosure may be provided.

Accordingly, those of ordinary skill in the art will recognize that various changes and modifications, / of the various embodiments described herein can be made without departing from the scope of the disclosure.

<FIG> is a view illustrating a user recognition system, according to an embodiment of the disclosure.

Referring to <FIG>, a user recognition device (or electronic apparatus) <NUM> may recognize a user <NUM> at a specified location (e.g., home) <NUM> and may provide a specified service depending on the recognized user.

According to an embodiment, the user recognition device <NUM> may recognize the user <NUM>, via a vision sensor. According to an embodiment, the user recognition device <NUM> may detect the user <NUM> via the vision sensor and may generate an image <NUM> in which the detected user <NUM> is included. According to an embodiment, the user recognition device <NUM> may recognize the user <NUM>, using the generated image.

According to an embodiment, when recognizing a user, the user recognition device <NUM> may provide a service corresponding to the recognized user. For example, the user recognition device <NUM> may control an internet of thing (IoT) device <NUM> to have a state set by the recognized user or may control the IoT device <NUM> to perform a specified function. For example, the IoT device <NUM> may be a device installed at the location (e.g., home) <NUM> where the user recognition device <NUM> is positioned.

According to an embodiment, the user recognition device <NUM> may provide a specified service via a cloud server <NUM>. For example, the user recognition device <NUM> may control the IOT device <NUM> through the cloud server <NUM> (e.g., an IOT cloud server). The user recognition device <NUM> may transmit information (or control information) for controlling the IOT device <NUM>, to the cloud server <NUM>. For example, the control information may include state information or operation information corresponding to the recognized user. The cloud server <NUM> may receive control information and may transmit a command according to the received control information, to the IoT device <NUM>.

According to an embodiment, the user recognition device <NUM> may be implemented in a variety of electronic apparatuses including the vision sensor. For example, the user recognition device <NUM> may be a smartphone, a tablet personal computer (PC), a desktop computer, a television (TV), a wearable device, or the like including the vision sensor. According to an embodiment, the user recognition device <NUM> may be included in a security system or a smart home system.

When the user recognition device <NUM> recognizes the user <NUM> using the RGB image obtained via the frame-based vision sensor, the user recognition device <NUM> may recognize the user <NUM> accurately, but the privacy of the user <NUM> may be infringed by extracting the features of the user's face, body, and other bodies. The privacy may be infringed when an RGB image including the user is transmitted to the server. Furthermore, when the user recognition device <NUM> recognizes the user <NUM> by using an image of the shape (or contour) of an object obtained via an event-based vision sensor, the privacy problem may be solved, but it may be difficult to accurately recognize the user.

The user recognition device <NUM> according to various embodiments of the disclosure may generate an image including the shape of an object to train the shape of the user of the generated image, and thus may accurately recognize the user.

<FIG> is a block diagram illustrating a configuration of a user recognition device, according to an embodiment of the disclosure.

Referring to <FIG>, the user recognition device <NUM> includes a communication interface <NUM> (e.g., a transceiver), a memory <NUM>, a dynamic vision sense (DVS) module <NUM>, and a processor (or at least one processor) <NUM>.

According to an embodiment, the communication interface <NUM> may be connected to an external device to transmit or receive data. For example, the communication interface <NUM> may be connected to a cloud server (e.g., the cloud server <NUM> of <FIG>) to receive data. For another example, the communication interface <NUM> may be connected to a user terminal to transmit or receive data. According to an embodiment, the communication interface <NUM> may include at least one of a wireless interface and a wired interface. For example, the wireless interface may include Bluetooth, near field communication (NFC), wireless-fidelity (Wi-Fi), or the like. The wired interface may include local area network (LAN), wide area network (WAN), power line communication, and plain old telephone service (POTS).

According to an embodiment, the memory <NUM> may include at least one database capable of storing data. For example, the memory <NUM> may include a database storing the image generated by the user recognition device <NUM>. For example, the database may be a database in which an image for training the shape of a specified user is stored. According to an embodiment, the memory <NUM> may include a nonvolatile memory for storing data. For example, the memory <NUM> may include a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or the like.

According to an embodiment, the DVS module <NUM> outputs an event signal based on the generated event. For example, the DVS module <NUM> may sense the motion of an object (e.g., the user) to output an event signal. According to an embodiment, the DVS module <NUM> may include a lens <NUM> and a DVS <NUM>.

According to an embodiment, the lens <NUM> may transmit the light reflected by the object to the DVS <NUM>. For example, the lens <NUM> may transmit the light reflected by the object by allowing the light to enter the inside of the user recognition device <NUM>.

According to an embodiment, the DVS <NUM> may detect the incident light through the lens <NUM> to output an event signal. The DVS <NUM> includes a plurality of sensing elements. Each of the plurality of sensing elements senses the incident light to output the event signal. The DVS <NUM> outputs the event signal from a sensing element in which light is changed. For example, a sensing element, in which the intensity of light increases, from among the plurality of sensing elements of the DVS <NUM> may output an on-event signal, and a sensing element in which the intensity of light decreases may output an off-event signal. According to an embodiment, the change in light detected through the DVS <NUM> may be caused by the motion of an object. As such, the DVS <NUM> may detect the motion of an object to output an event signal.

According to an embodiment, the processor <NUM> may receive the event signal output from the DVS module <NUM> and may output an image based on the received event signal.

The processor <NUM> stores information about a point in time when light is changed, in the event map based on the received event signal. The event map includes a plurality of map elements corresponding to the plurality of sensing elements of the DVS <NUM>. The processor <NUM> stores time information in a map element corresponding to an element in which light is changed, based on an event signal. According to an embodiment, the processor <NUM> may store information about a point in time when light has been recently changed, in an event map (e.g., a two-dimensional event map). Furthermore, the processor <NUM> may store not only the information about a point in time when light has been recently changed, but also information about a point in time when light was changed in the past, in an event map (e.g., a three-dimensional event map).

The processor <NUM> generates an image based on the event map. The processor <NUM> generates an image based on time information stored in the event map. The processor <NUM> generates an image by displaying a specified value in a pixel corresponding to an element of a map in which time information is stored. According to an embodiment, the processor <NUM> may generate an image based on time information within a specified time range among time information stored in the event map. For example, the processor <NUM> may generate an image based on time information within a specified time from the present.

According to an embodiment, the processor <NUM> may store the generated image in a database of the memory <NUM>. For example, the processor <NUM> may store the image generated under the specified condition, in the database to train the shape of the specified user (or a specific user).

According to an embodiment, the processor <NUM> may store the generated image in the database when receiving identification information for distinguishing the user from the external electronic apparatus via the communication interface <NUM>. For example, the external electronic apparatus may receive identification information from a key, a user terminal, or a beacon including a radio frequency integrated circuit (RFIC), to perform a specified operation. The specified operation may be an operation of setting or disabling security. This will be described in detail with reference to <FIG>.

According to another embodiment, the processor <NUM> may store an image generated based on the event detected in a specified space, in a database. The processor <NUM> may store the generated image based on the feedback information of the user, in the database. This will be described in detail with reference to <FIG>.

According to an embodiment, the processor <NUM> may train the shape of the specified user included in each of a plurality of images stored in the database of the memory <NUM>. For example, the processor <NUM> may identify the shape of a specified user included in each of the plurality of images stored in the database and may generate shape information for recognizing the specified user, based on the identified shape.

According to an embodiment, the processor <NUM> identifies the shape of the user included in an image. For example, the processor <NUM> may identify the shape of the user, using a method such as regions convolutional neural network (RCNN), fast regions convolutional neural network (FRCNN), or the like.

According to an embodiment, the processor <NUM> may extract the feature of an image to identify the user's shape. According to an embodiment, the processor <NUM> may determine the suggestion region of the image generated based on the extracted feature and may refine the region of interest (ROI) capable of including the user's shape of the determined suggestion region. According to an embodiment, the processor <NUM> may identify the shape of the user included in the ROI, using a classification model capable of recognizing the user. For example, the processor <NUM> may identify the shape of the user, using a support vector machine (SVM) classifier. According to an embodiment, the processor <NUM> may track the shape of a user included in a plurality of images.

According to an embodiment, the processor <NUM> may generate shape information for recognizing the specified user based on the shape information of the identified user. For example, the processor <NUM> may extract feature of the identified shape and may generate shape information for recognizing the specified user based on the extracted feature.

According to an embodiment, the processor <NUM> may recognize the specified user using the generated shape information. For example, the processor <NUM> may recognize the specified user included in the image, using the generated shape information. For example, the image may be an image generated based on an event detected through the DVS module <NUM>.

According to another embodiment, the processor <NUM> may generate shape information for recognizing the specified user through a cloud server (or an external server). The processor <NUM> may transmit the image generated to the cloud server through the communication interface <NUM> and may receive shape information generated from the cloud server. For example, the cloud server may similarly perform an operation in which the processor <NUM> generates shape information. The cloud server may generate shape information for recognizing the user by training the shape of the user included in the image generated under the specified condition. Because the user recognition device <NUM> has limitations in training the user's shape, the user recognition device <NUM> may generate more accurate shape information through the cloud server.

According to an embodiment, the processor <NUM> may provide a service corresponding to a recognized user. For example, the processor <NUM> may control at least one IoT device (e.g., the IoT device <NUM> of <FIG>) based on the recognized user. For example, the processor <NUM> may control the at least one IoT device to have a specified state or to perform the specified operation, based on the recognized user. According to an embodiment, the processor <NUM> may control at least one IoT device through a cloud server (e.g., the cloud server <NUM> of <FIG>).

As such, the user recognition device <NUM> may train the shape of a specified user, using an image including only the shape of an object, may generate shape information for recognizing a specified user, and may recognize the specified user based on the generated shape information, thereby correctly recognizing the specified user without any problem of privacy.

<FIG> is a flowchart illustrating a user recognizing method of a user recognition device, according to an embodiment of the disclosure.

Referring to <FIG>, the user recognition device <NUM> may recognize a specified user, using the image generated based on an event detected through the DVS module <NUM>.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> (e.g., the processor <NUM>) may receive an event signal from the DVS module <NUM>. The DVS module <NUM> may generate an event signal by detecting the change in light by the motion of an object.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may generate a plurality of images based on the received event signal. For example, the plurality of images may include the shape of a moving object.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may store an image generated under the specified condition, in a database. For example, the specified condition may be the case where identification information for distinguishing a user is received from an external electronic apparatus (e.g., a security setting device) or the case where an event is detected in the specified space.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may identify the shape of the specified user included in each of the plurality of images. For example, the user recognition device <NUM> may extract the features of a plurality of images and may identify the shape of the user included in the ROI determined based on the extracted features.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may generate shape information for recognizing the specified user, based on the identified shape. According to an embodiment, the user recognition device <NUM> may generate shape information for recognizing a specified user, by training the shape of a specified user included in a plurality of images.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may recognize a specified user, using the shape information. For example, the user recognition device <NUM> may recognize the specified user included in the image, using the generated shape information. For example, the image may be an image generated based on an event detected through the DVS module <NUM>.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may control at least one IoT device, depending on the recognized user. For example, the user recognition device <NUM> may control at least one IOT device through a cloud server.

<FIG> is a view illustrating how a user recognition device recognizes a user, using identification information of a user received from an external device, according to an embodiment of the disclosure.

Referring to <FIG>, the user recognition device <NUM> may train the shape of the specified user <NUM>, using identification information (e.g., identity (ID)) of the user <NUM> received from an external electronic apparatus <NUM>.

According to an embodiment, the external electronic apparatus <NUM> may receive user identification information to perform a specified operation. For example, the specified operation may be an operation for setting or disabling security at the specified location (e.g., home) <NUM>. The external electronic apparatus <NUM> may be a user recognition device included in a security system. According to an embodiment, the external electronic apparatus <NUM> may receive identification information of the user <NUM> from a key, a user terminal, a beacon, or the like including an RFIC. For example, the external electronic apparatus <NUM> may receive the identification information by the tagging operation of the user <NUM>.

According to an embodiment, the user recognition device <NUM> may receive identification information for distinguishing the user <NUM> from the external electronic apparatus <NUM>. For example, when receiving a user input for performing a specified operation (e.g., setting or disabling security), the external electronic apparatus <NUM> may transmit the identification information included in the user input, to the user recognition device <NUM>.

According to an embodiment, the user recognition device <NUM> may store the image <NUM> generated at a point in time when the user recognition device <NUM> receives the identification information from the external electronic apparatus <NUM>, in a database. For example, the user recognition device <NUM> may store the plurality of images <NUM> generated within a specified time from a point in time when the user recognition device <NUM> receives the identification information, in the database. For example, the plurality of images <NUM> may be generated based on the event detected via a DVS module (e.g., the DVS module <NUM> of <FIG>).

According to an embodiment, the user recognition device <NUM> may distinguish and store the images <NUM> generated depending on the received identification information. In other words, the user recognition device <NUM> may store the images <NUM> generated to correspond to the identification information. As such, the user recognition device <NUM> may distinguish and train each of a plurality of users capable of being recognized at a specified place.

According to an embodiment, when only a single user shape is included in the generated images <NUM>, the user recognition device <NUM> may store the generated images <NUM> in the database. When the generated image <NUM> includes the plurality of user's shapes, it may be difficult for the user recognition device <NUM> to distinguish the shape of the user corresponding to the received identification information, and thus the user recognition device <NUM> may store an image including only a single user shape, in the database.

As such, the user recognition device <NUM> may store the images <NUM> including the shape of a specified user (or specific user), in the database.

According to an embodiment, the user recognition device <NUM> may train the shape of the specified user <NUM> included in each of a plurality of images stored in the database. According to an embodiment, the user recognition device <NUM> may generate shape information capable of recognizing the specified user <NUM>, based on the trained information (or data). As such, the user recognition device <NUM> may recognize the specified user <NUM> with a high probability, using the generated shape information.

<FIG> is a flowchart illustrating a method of training a shape of a user included in an image, using identification information of a user received from an external device, according to an embodiment of the disclosure.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may receive identification information of the user <NUM> from the external electronic apparatus <NUM>.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may generate the plurality of images <NUM> within a specified time from a point in time when the user recognition device <NUM> receives the identification information.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may determine whether each of the generated plurality of images <NUM> includes a single user shape. According to an embodiment, when two or more shapes of a user are included in each of the plurality of images <NUM> (No), in operation <NUM>, the user recognition device <NUM> may stand by to receive the identification information of the user <NUM>.

When the single shape of a user is included in each of the plurality of images <NUM> (Yes), in operation <NUM>, the user recognition device <NUM> stores the plurality of images <NUM> in the database.

As such, the user recognition device <NUM> may generate shape information for recognizing the specified user <NUM> by training the shape of the user included in the image stored in the database. The user recognition device <NUM> may recognize the specified user <NUM>, using the shape information.

<FIG> is a view illustrating how a user recognition device recognizes a user based on an event detected in a specified space, according to an embodiment of the disclosure.

Referring to <FIG>, the user recognition device <NUM> may generate the image <NUM> based on the detected event in the specified space and may train the shape of the specified user, using the generated images <NUM>.

According to an embodiment, the user recognition device <NUM> may detect an event occurring in a specified space (or a specific space). For example, an event occurring in a space of a high frequency at which a specified user (or a specific user) appears may be detected in the specified space. For example, the user recognition device <NUM> may detect an event occurring in a second room 40a of a house <NUM>. The second room 40a of the house <NUM> may correspond to a personal space of a specified user. Because the other space (e.g., a living room, a kitchen, or a garage) of the house <NUM> may correspond to a shared space of a plurality of users, it may be difficult for the user recognition device <NUM> to generate an image including only the shape of the specified user.

According to an embodiment, the user recognition device <NUM> may generate the plurality of images <NUM> based on the event occurring in the specified space.

According to an embodiment, the user recognition device <NUM> may transmit the generated plurality of images <NUM> to a user terminal <NUM>. For example, when the shape of the user <NUM> is included in the plurality of images <NUM> more than the specified number of times, the user recognition device <NUM> may transmit the plurality of images <NUM> to the user terminal <NUM>.

According to an embodiment, the user terminal <NUM> may receive a feedback for determining whether the shape of the specified user (e.g., a user of the user terminal <NUM>) <NUM> is included in the received plurality of images <NUM>. According to an embodiment, the user terminal <NUM> may transmit information (or feedback information) about the received feedback, to the user recognition device <NUM>.

According to an embodiment, the user recognition device <NUM> may store the plurality of images <NUM> in a database based on the received feedback information. For example, when receiving the feedback information for determining that the plurality of images <NUM> include the specified user (e.g., a user of the user terminal <NUM>) <NUM>, the user recognition device <NUM> may store the plurality of images <NUM> in the database.

According to an embodiment, the user recognition device <NUM> may train the shape of the specified user <NUM> included in the plurality of images stored in the database. According to an embodiment, the user recognition device <NUM> may generate shape information capable of recognizing the specified user <NUM>, based on the trained information (or data). As such, the user recognition device <NUM> may recognize the specified user <NUM> with a high probability, using the generated shape information.

<FIG> is a view illustrating a user terminal displaying an image received from a user recognition device to receive feedback information, according to an embodiment of the disclosure.

Referring to <FIG>, the user terminal <NUM> may provide a user with a plurality of images <NUM> received from the user recognition device <NUM> and may receive feedback information for determining whether the shape of the user is included in the plurality of images <NUM>.

According to an embodiment, the user terminal <NUM> may execute an application program for receiving the feedback input of the user. According to an embodiment, the user terminal <NUM> may display a user interface (UI) <NUM> for receiving a user input, in a display <NUM>. The user terminal <NUM> may display the received plurality of images <NUM> via the UI <NUM> and may receive the feedback information of the user via an object (e.g., a virtual button) <NUM>.

<FIG> is a flowchart illustrating a method of training a shape of a user based on an event detected in a specified space, according to an embodiment of the disclosure.

Referring to <FIG>, in operation <NUM>, the user recognition device <NUM> may detect an event occurring in a specified space.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may generate the plurality of images <NUM> based on the event detected in the specified space.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may determine whether the user's shape is included in the plurality of images <NUM> more than the specified number of times. According to an embodiment, when the user's shape is included in the plurality of images <NUM> less than the specified number of times (No), in operation <NUM>, the user recognition device <NUM> may stand by to detect an event occurring in a specified space of the user <NUM>.

According to an embodiment, when the user's shape is included in the plurality of images <NUM> more than the specified number of times (Yes), in operation <NUM>, the user recognition device <NUM> may transmit the plurality of images <NUM> to the user terminal <NUM>.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may receive feedback information for determining whether the shape of the specified user <NUM> is included in the plurality of images <NUM>, from the user terminal <NUM>. For example, the specified user <NUM> may be a user of the user terminal <NUM>.

According to an embodiment, in operation <NUM>, the user recognition device <NUM> may store the plurality of images <NUM> in a database based on the received feedback information.

As such, the user recognition device <NUM> may generate shape information for recognizing the specified user <NUM> by training the shape of the user <NUM> included in the image stored in the database. The user recognition device <NUM> may recognize the specified user <NUM>, using the shape information.

<FIG> is a view illustrating how a user recognition device controls an IoT depending on the recognized user, according to an embodiment of the disclosure.

Referring to <FIG>, when recognizing the user <NUM> in the specified group, the user recognition device <NUM> may control the IoT device <NUM> differently depending on the recognized user <NUM>.

According to an embodiment, the user recognition device <NUM> may be configured to control the IoT device <NUM> differently for the respective user <NUM> in the specified group. For example, the specified group may be a group of users (e.g., family (users A to D)) capable of being recognized in a specified space. According to an embodiment, a user defined rule may be stored in the user recognition device <NUM> (e.g., the memory <NUM>) to control the IoT device <NUM> differently for the respective user <NUM>. For example, the user defined rule not only may control power (ON/OFF) of the IoT device <NUM>, but also may store a setting value for operating in a specified state (e.g., set temperature, playing music genre, or the like).

According to an embodiment, the user <NUM> may store the user defined rule in the user recognition device <NUM> via a user terminal <NUM>. For example, the user terminal <NUM> may display a UI for setting the state of the IoT device <NUM> on the display and may receive a user input (e.g., a touch input) for setting a rule for controlling the IOT device <NUM>.

According to an embodiment, the user recognition device <NUM> may control the IoT device <NUM> depending on the rule (user defined rule) defined for the respective user <NUM>, for each recognized user. For example, when recognizing user A, the user recognition device <NUM> may allow an air conditioner to operate in a state where the air conditioner is set to <NUM>, may allow a light lamp in a specified space to be turned off, and may allow a speaker to play jazz music, depending on the first user defined rule. When recognizing user B, the user recognition device <NUM> may allow the air conditioner to be turned off, may allow the light lamp in the specified space to be turned on, and may allow the speaker to play classic music, depending on the second user defined rule. For example, when recognizing user C, the user recognition device <NUM> may allow the air conditioner to operate in a state where the air conditioner is set to <NUM>, may allow the light lamp in the specified space to be turned on, and may allow a speaker to be turned off, depending on the third user defined rule. When recognizing user D, the user recognition device <NUM> may allow the air conditioner to be turned off, may allow the light lamp in the specified space to be turned off, and may allow the speaker to play random music, depending on the fourth user defined rule.

As such, the user recognition device <NUM> may provide a service suitable for the user's preference, by controlling the IoT device <NUM> depending on the user <NUM> in the specified group.

<FIG> is a block diagram of an electronic device in a network environment according to an embodiment of the disclosure.

Referring to <FIG>, an electronic device <NUM> may communicate with an electronic device <NUM> through a first network <NUM> (e.g., a short-range wireless communication) or may communicate with an electronic device <NUM> or a server <NUM> through a second network <NUM> (e.g., a long-distance wireless communication) in a network environment <NUM>. According to an embodiment, the electronic device <NUM> may communicate with the electronic device <NUM> through the server <NUM>. According to an embodiment, the electronic device <NUM> may include a processor <NUM>, a memory <NUM>, an input device <NUM>, a sound output device <NUM>, a display device <NUM>, an audio module <NUM>, a sensor module <NUM>, an interface <NUM>, a haptic module <NUM>, a camera module <NUM>, a power management module <NUM>, a battery <NUM>, a communication module <NUM>, a subscriber identification module <NUM>, and an antenna module <NUM>. According to some embodiments, at least one (e.g., the display device <NUM> or the camera module <NUM>) among components of the electronic device <NUM> may be omitted or other components may be added to the electronic device <NUM>. According to some embodiments, some components may be integrated and implemented as in the case of the sensor module <NUM> (e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) embedded in the display device <NUM> (e.g., a display).

The processor <NUM> may operate, for example, software (e.g., a program <NUM>) to control at least one of other components (e.g., a hardware or software component) of the electronic device <NUM> connected to the processor <NUM> and may process and compute a variety of data. The processor <NUM> may load a command set or data, which is received from other components (e.g., the sensor module <NUM> or the communication module <NUM>), into a volatile memory <NUM>, may process the loaded command or data, and may store result data into a nonvolatile memory <NUM>. According to an embodiment, the processor <NUM> may include a main processor <NUM> (e.g., a central processing unit or an application processor) and an auxiliary processor <NUM> (e.g., a graphic processing device, an image signal processor, a sensor hub processor, or a communication processor), which operates independently from the main processor <NUM>, additionally or alternatively uses less power than the main processor <NUM>, or is specified to a designated function. In this case, the auxiliary processor <NUM> may operate separately from the main processor <NUM> or embedded.

In this case, the auxiliary processor <NUM> may control, for example, at least some of functions or states associated with at least one component (e.g., the display device <NUM>, the sensor module <NUM>, or the communication module <NUM>) among the components of the electronic device <NUM> instead of the main processor <NUM> while the main processor <NUM> is in an inactive (e.g., sleep) state or together with the main processor <NUM> while the main processor <NUM> is in an active (e.g., an application execution) state. According to an embodiment, the auxiliary processor <NUM> (e.g., the image signal processor or the communication processor) may be implemented as a part of another component (e.g., the camera module <NUM> or the communication module <NUM>) that is functionally related to the auxiliary processor <NUM>. The memory <NUM> may store a variety of data used by at least one component (e.g., the processor <NUM> or the sensor module <NUM>) of the electronic device <NUM>, for example, software (e.g., the program <NUM>) and input data or output data with respect to commands associated with the software. The memory <NUM> may include the volatile memory <NUM> or the nonvolatile memory <NUM>.

The program <NUM> may be stored in the memory <NUM> as software and may include, for example, an operating system <NUM>, a middleware <NUM>, or an application <NUM>.

The input device <NUM> may be a device for receiving a command or data, which is used for a component (e.g., the processor <NUM>) of the electronic device <NUM>, from an outside (e.g., a user) of the electronic device <NUM> and may include, for example, a microphone, a mouse, or a keyboard.

The sound output device <NUM> may be a device for outputting a sound signal to the outside of the electronic device <NUM> and may include, for example, a speaker used for general purposes, such as multimedia play or recordings play, and a receiver used only for receiving calls. According to an embodiment, the receiver and the speaker may be either integrally or separately implemented.

The display device <NUM> may be a device for visually presenting information to the user of the electronic device <NUM> and may include, for example, a display, a hologram device, or a projector and a control circuit for controlling a corresponding device. According to an embodiment, the display device <NUM> may include a touch circuitry or a pressure sensor for measuring an intensity of pressure on the touch.

The audio module <NUM> may convert a sound and an electrical signal in dual directions. According to an embodiment, the audio module <NUM> may obtain the sound through the input device <NUM> or may output the sound through an external electronic device (e.g., the electronic device <NUM> (e.g., a speaker or a headphone)) wired or wirelessly connected to the sound output device <NUM> or the electronic device <NUM>.

The sensor module <NUM> may generate an electrical signal or a data value corresponding to an operating state (e.g., power or temperature) inside or an environmental state outside the electronic device <NUM>. The sensor module <NUM> may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface <NUM> may support a designated protocol wired or wirelessly connected to the external electronic device (e.g., the electronic device <NUM>). According to an embodiment, the interface <NUM> may include, for example, an HDMI (high-definition multimedia interface), a USB (universal serial bus) interface, an SD card interface, or an audio interface.

A connecting terminal <NUM> may include a connector that physically connects the electronic device <NUM> to the external electronic device (e.g., the electronic device <NUM>), for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

The haptic module <NUM> may convert an electrical signal to a mechanical stimulation (e.g., vibration or movement) or an electrical stimulation perceived by the user through tactile or kinesthetic sensations.

The camera module <NUM> may shoot a still image or a video image. According to an embodiment, the camera module <NUM> may include, for example, at least one lens, an image sensor, an image signal processor, or a flash.

The power management module <NUM> may be a module for managing power supplied to the electronic device <NUM> and may serve as at least a part of a power management integrated circuit (PMIC).

The battery <NUM> may be a device for supplying power to at least one component of the electronic device <NUM> and may include, for example, a non-rechargeable (primary) battery, a rechargeable (secondary) battery, or a fuel cell.

The communication module <NUM> may establish a wired or wireless communication channel between the electronic device <NUM> and the external electronic device (e.g., the electronic device <NUM>, the electronic device <NUM>, or the server <NUM>) and support communication execution through the established communication channel. The communication module <NUM> may include at least one communication processor operating independently from the processor <NUM> (e.g., the application processor) and supporting the wired communication or the wireless communication. According to an embodiment, the communication module <NUM> may include a wireless communication module <NUM> (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module <NUM> (e.g., an LAN (local area network) communication module or a power line communication module) and may communicate with the external electronic device using a corresponding communication module among them through the first network <NUM> (e.g., the short-range communication network such as a Bluetooth, a WiFi direct, or an IrDA (infrared data association)) or the second network <NUM> (e.g., the long-distance wireless communication network such as a cellular network, an internet, or a computer network (e.g., LAN or WAN)). The above-mentioned various communication modules <NUM> may be implemented into one chip or into separate chips, respectively.

According to an embodiment, the wireless communication module <NUM> may identify and authenticate the electronic device <NUM> using user information stored in the subscriber identification module <NUM> in the communication network.

The antenna module <NUM> may include one or more antennas to transmit or receive the signal or power to or from an external source. According to an embodiment, the communication module <NUM> (e.g., the wireless communication module <NUM>) may transmit or receive the signal to or from the external electronic device through the antenna suitable for the communication method.

Some components among the components may be connected to each other through a communication method (e.g., a bus, a GPIO (general purpose input/output), an SPI (serial peripheral interface), or an MIPI (mobile industry processor interface)) used between peripheral devices to exchange signals (e.g., a command or data) with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device <NUM> and the external electronic device <NUM> through the server <NUM> connected to the second network <NUM>. Each of the electronic devices <NUM> and <NUM> may be the same or different types as or from the electronic device <NUM>. According to an embodiment, all or some of the operations performed by the electronic device <NUM> may be performed by another electronic device or a plurality of external electronic devices. When the electronic device <NUM> performs some functions or services automatically or by request, the electronic device <NUM> may request the external electronic device to perform at least some of the functions related to the functions or services, in addition to or instead of performing the functions or services by itself. The external electronic device receiving the request may carry out the requested function or the additional function and transmit the result to the electronic device <NUM>. The electronic device <NUM> may provide the requested functions or services based on the received result as is or after additionally processing the received result. To this end, for example, a cloud computing, distributed computing, or client-server computing technology may be used.

The electronic device according to various embodiments disclosed in the present disclosure may be various types of devices. The electronic device may include, for example, at least one of a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a mobile medical appliance, a camera, a wearable device, or a home appliance. The electronic device according to an embodiment of the present disclosure should not be limited to the above-mentioned devices.

It should be understood that various embodiments of the present disclosure and terms used in the embodiments do not intend to limit technologies disclosed in the present disclosure to the particular forms disclosed herein; rather, the present disclosure should be construed to cover various modifications, equivalents, and/or alternatives of embodiments of the present disclosure. With regard to description of drawings, similar components may be assigned with similar reference numerals. As used herein, singular forms may include plural forms as well unless the context clearly indicates otherwise. In the present disclosure disclosed herein, the expressions "A or B", "at least one of A or/and B", "A, B, or C" or "one or more of A, B, or/and C", and the like used herein may include any and all combinations of one or more of the associated listed items. The expressions "a first", "a second", "the first", or "the second", used in herein, may refer to various components regardless of the order and/or the importance, but do not limit the corresponding components. The above expressions are used merely for the purpose of distinguishing a component from the other components. It should be understood that when a component (e.g., a first component) is referred to as being (operatively or communicatively) "connected," or "coupled," to another component (e.g., a second component), it may be directly connected or coupled directly to the other component or any other component (e.g., a third component) may be interposed between them.

The term "module" used herein may represent, for example, a unit including one or more combinations of hardware, software and firmware. The term "module" may be interchangeably used with the terms "logic", "logical block", "part" and "circuit". The "module" may be a minimum unit of an integrated part or may be a part thereof. The "module" may be a minimum unit for performing one or more functions or a part thereof. For example, the "module" may include an application-specific integrated circuit (ASIC).

Various embodiments of the present disclosure may be implemented by software (e.g., the program <NUM>) including an instruction stored in a machine-readable storage media (e.g., an internal memory <NUM> or an external memory <NUM>) readable by a machine (e.g., a computer). The machine may be a device that calls the instruction from the machine-readable storage media and operates depending on the called instruction and may include the electronic device (e.g., the electronic device <NUM>). When the instruction is executed by the processor (e.g., the processor <NUM>), the processor may perform a function corresponding to the instruction directly or using other components under the control of the processor. The instruction may include a code generated or executed by a compiler or an interpreter. The machine-readable storage media may be provided in the form of non-transitory storage media. Here, the term "non-transitory", as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency.

According to an embodiment, the method according to various embodiments disclosed in the present disclosure may be provided as a part of a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or may be distributed only through an application store (e.g., a Play Store<IMG>). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or generated in a storage medium such as a memory of a manufacturer's server, an application store's server, or a relay server.

Claim 1:
An electronic apparatus comprising:
a communication interface (<NUM>);
a dynamic vision sensor, DVS, (<NUM>) comprising a plurality of sensing elements configured to output an event signal when detecting a change in incident light;
a memory (<NUM>) including a database in which one or more images are stored; and
at least one processor (<NUM>) configured to:
based on event signals output by the DVS, store information about points in time when the change in the light occurred in an event map as a time information, wherein the event map includes a plurality of map elements corresponding to the plurality of sensing elements,
based on the time information which is stored in the event map, generate a plurality of images, by displaying a specified value in a pixel, the pixel corresponding to time information stored in a map element,
when each of the plurality of images includes a shape of one single user, store the plurality of images, in the database,
identify the shape of the user included in each of the plurality of images stored in the database, and
generate shape information for recognizing the user based on the identified shape of the user.