Electronic apparatus and operation method thereof

Provided is a method of storing information on a face of a passenger in a vehicle in association with a terminal of the passenger, and an electronic apparatus therefor. In the present disclosure, at least one of an electronic apparatus, a vehicle, a vehicle terminal, and an autonomous vehicle may be connected or converged with an artificial intelligence (AI) module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device associated with a 5G service, and the like.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of Korean Patent Application No. 10-2019-0173119, filed on Dec. 23, 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.

BACKGROUND

This disclosure relates to an electronic apparatus and an operation method thereof.

2. Description of the Related Art

A vehicle environment in which an in-vehicle device and a passenger terminal are interlocked through a communication connection is becoming more common and various studies are being conducted to increase user convenience in such a vehicle environment.

An autonomous vehicle refers to a vehicle equipped with an autonomous driving device that recognizes an environment around the vehicle and a state of the vehicle to control driving of the vehicle based on the environment and the state. With progress in research on autonomous vehicles, studies on various services that may increase a user's convenience using the autonomous vehicle are also being conducted.

SUMMARY

An aspect provides an electronic apparatus and an operation method thereof. Technical goals to be achieved through the example embodiments are not limited to the technical goals as described above, and other technical tasks can be inferred from the following example embodiments.

According to an aspect, there is provided an operation method of an electronic apparatus, the method including recognizing a face of a passenger in a vehicle through a sensor, acquiring information on a position of the passenger and information on a position of a terminal in the vehicle, and storing, when a difference between the position of the passenger and the position of the vehicle is within a preset distance, information on the recognized face in association with the terminal based on the acquired information.

According to another aspect, there is also provided an electronic apparatus including an interface configured to acquire sensing information of a face of a passenger in a vehicle from a sensor, and a processor configured to recognize the face of the passenger based on the acquired sensing information, and store information on the recognized face in association with the terminal based on information on a position of the passenger acquired through the interface and information on a position of a terminal in the vehicle when a difference between the position of the passenger and the position of the terminal is within a preset distance.

According to another aspect, there is also provided a vehicle including a sensor, and an electronic apparatus that recognizes a face of a passenger in the vehicle based on sensing information acquired from the sensor, acquires information on a position of the passenger and information on a position of a terminal in the vehicle, and stores, when a difference between the position of the passenger and the position of the vehicle is within a preset distance, information on the recognized face in association with the terminal based on the acquired information.

According to another aspect, there is also provided a non-volatile computer-readable recording medium including a computer program for performing the above-described method.

Specific details of example embodiments are included in the detailed description and drawings.

DETAILED DESCRIPTION

The terms used in the embodiments are selected, as much as possible, from general terms that are widely used at present while taking into consideration the functions obtained in accordance with the present disclosure, but these terms may be replaced by other terms based on intentions of those skilled in the art, customs, emergence of new technologies, or the like. Also, in a particular case, terms that are arbitrarily selected by the applicant of the present disclosure may be used. In this case, the meanings of these terms may be described in corresponding description parts of the disclosure. Accordingly, it should be noted that the terms used herein should be construed based on practical meanings thereof and the whole content of this specification, rather than being simply construed based on names of the terms.

In the entire specification, when an element is referred to as “including” another element, the element should not be understood as excluding other elements so long as there is no special conflicting description, and the element may include at least one other element. In addition, the terms “unit” and “module”, for example, may refer to a component that exerts at least one function or operation, and may be realized in hardware or software, or may be realized by combination of hardware and software.

In addition, in this specification, “artificial intelligence (AI)” refers to the field of studying artificial intelligence or a methodology capable of making the artificial intelligence, and “machine learning” refers to the field of studying methodologies that define and solve various problems handled in the field of artificial intelligence. The machine learning is also defined as an algorithm that enhances performance for a certain operation through a steady experience with respect to the operation.

An “artificial neural network (ANN)” may refer to a general model for use in the machine learning, which is composed of artificial neurons (nodes) forming a network by synaptic connection and has problem solving ability. The artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process of updating model parameters, and an activation function of generating an output value.

The artificial neural network may include an input layer and an output layer, and may selectively include one or more hidden layers. Each layer may include one or more neurons, and the artificial neural network may include a synapse that interconnects neurons. In the artificial neural network, each neuron may output the value of an activation function concerning signals input through the synapse, weights, and deflection thereof.

The model parameters refer to parameters determined by learning, and include weights for synaptic connection and deflection of neurons, for example. Then, hyper-parameters refer to parameters to be set before learning in a machine learning algorithm, and include a learning rate, the number of repetitions, the size of a mini-batch, and an initialization function, for example.

It can be said that the purpose of learning of the artificial neural network is to determine a model parameter that minimizes a loss function. The loss function may be used as an index for determining an optimal model parameter in a learning process of the artificial neural network.

The machine learning may be classified, according to a learning method, into supervised learning, unsupervised learning, and reinforcement learning.

The supervised learning refers to a learning method for an artificial neural network in the state in which a label for learning data is given. The label may refer to a correct answer (or a result value) to be deduced by the artificial neural network when learning data is input to the artificial neural network. The unsupervised learning may refer to a learning method for the artificial neural network in the state in which no label for learning data is given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.

The machine learning realized by a deep neural network (DNN) including multiple hidden layers among artificial neural networks is also called deep learning, and the deep learning is a part of the machine learning. In the following description, the machine learning is used as a meaning including the deep learning.

In addition, in this specification, a vehicle may be an autonomous vehicle. “Autonomous driving” refers to a self-driving technology, and an “autonomous vehicle” refers to a vehicle that performs driving without a user's operation or with a user's minimum operation. In addition, the autonomous vehicle may refer to a robot having an autonomous driving function.

For example, autonomous driving may include all of a technology of maintaining the lane in which a vehicle is driving, a technology of automatically adjusting a vehicle speed such as adaptive cruise control, a technology of causing a vehicle to automatically drive in a given route, and a technology of automatically setting a route, along which a vehicle drives, when a destination is set.

Here, a vehicle may include all of a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may be meant to include not only an automobile but also a train and a motorcycle, for example.

In the following description, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily carry out the present disclosure. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein.

Hereinafter, example embodiments of the present disclosure will be described with reference to the drawings.

FIG. 1illustrates an AI device according to an example embodiment.

The AI device100may be realized into, for example, a stationary appliance or a movable appliance, such as a TV, a projector, a cellular phone, a smartphone, a desktop computer, a laptop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a digital signage, a robot, a vehicle, or an X reality (XR) device.

Referring toFIG. 1, the AI device100may include a communication part110, an input part120, a learning processor130, a sensing part140, an output part150, a memory170, and a processor180. However, not all components shown inFIG. 1are essential components of the AI device100. The AI device may be implemented by more components than those illustrated inFIG. 1, or the AI device may be implemented by fewer components than those illustrated inFIG. 1.

The communication part110may transmit and receive data to and from external devices, such as other AI devices100ato100eand an AI server200, using wired/wireless communication technologies. For example, the communication part110may transmit and receive sensor information, user input, learning models, and control signals, for example, to and from external devices.

At this time, the communication technology used by the communication part110may be, for example, a global system for mobile communication (GSM), code division multiple access (CDMA), long term evolution (LTE), 5G, wireless LAN (WLAN), wireless-fidelity (Wi-Fi), Bluetooth, radio frequency identification (RFID), infrared data association (IrDA), ZigBee, or near field communication (NFC).

The input part120may acquire various types of data.

At this time, the input part120may include a camera for the input of an image signal, a microphone for receiving an audio signal, and a user input part for receiving information input by a user, for example. Here, the camera or the microphone may be handled as a sensor, and a signal acquired from the camera or the microphone may be referred to as sensing data or sensor information.

The input part120may acquire, for example, input data to be used when acquiring an output using learning data for model learning and a learning model. The input part120may acquire unprocessed input data, and in this case, the processor180or the learning processor130may extract an input feature as pre-processing for the input data.

The learning processor130may cause a model configured with an artificial neural network to learn using the learning data. Here, the learned artificial neural network may be called a learning model. The learning model may be used to deduce a result value for newly input data other than the learning data, and the deduced value may be used as a determination base for performing any operation.

At this time, the learning processor130may perform AI processing along with a learning processor240of the AI server200.

At this time, the learning processor130may include a memory integrated or embodied in the AI device100. Alternatively, the learning processor130may be realized using the memory170, an external memory directly coupled to the AI device100, or a memory held in an external device.

The sensing part140may acquire at least one of internal information of the AI device100, environmental information around the AI device100, and user information using various sensors.

At this time, the sensors included in the sensing part140may be a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, a radar, and a temperature sensor, for example.

The output part150may generate, for example, a visual output, an auditory output, or a tactile output.

At this time, the output part150may include, for example, a display that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory170may store data which assists various functions of the AI device100. For example, the memory170may store input data acquired by the input part120, learning data, learning models, and learning history, for example. The memory170may include a storage medium of at least one type among a flash memory, a hard disk, a multimedia card micro type memory, a card type memory (e.g., SD or XD memory), a random access memory (RAM) a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disc, and an optical disc.

The processor180may determine at least one executable operation of the AI device100based on information determined or generated using a data analysis algorithm or a machine learning algorithm. Then, the processor180may control constituent elements of the AI device100to perform the determined operation.

To this end, the processor180may request, search, receive, or utilize data of the learning processor130or the memory170, and may control the constituent elements of the AI device100so as to execute a predictable operation or an operation that is deemed desirable among the at least one executable operation.

At this time, when connection of an external device is required to perform the determined operation, the processor180may generate a control signal for controlling the external device and may transmit the generated control signal to the external device.

The processor180may acquire intention information with respect to user input and may determine a user request based on the acquired intention information.

At this time, the processor180may acquire intention information corresponding to the user input using at least one of a speech to text (STT) engine for converting voice input into a character string and a natural language processing (NLP) engine for acquiring natural language intention information.

At this time, at least a part of the STT engine and/or the NLP engine may be configured with an artificial neural network learned according to a machine learning algorithm. Then, the STT engine and/or the NLP engine may have learned by the learning processor130, may have learned by a learning processor240of the AI server200, or may have learned by distributed processing of these processors.

The processor180may collect history information including, for example, the content of an operation of the AI device100or feedback of the user with respect to an operation, and may store the collected information in the memory170or the learning processor130, or may transmit the collected information to an external device such as the AI server200. The collected history information may be used to update a learning model.

The processor180may control at least some of the constituent elements of the AI device100in order to drive an application program stored in the memory170. Moreover, the processor180may combine and operate two or more of the constituent elements of the AI device100for the driving of the application program.

FIG. 2illustrates an AI server according to an example embodiment.

Referring toFIG. 2, an AI server200may refer to a device that causes an artificial neural network to learn using a machine learning algorithm or uses the learned artificial neural network. Here, the AI server200may be constituted of multiple servers to perform distributed processing, and may be defined as a 5G network. At this time, the AI server200may be included as a constituent element of the AI device100so as to perform at least a part of AI processing together with the AI device.

The AI server200may include a communication part210, a memory230, a learning processor240, and a processor260.

The communication part210may transmit and receive data to and from an external device such as the AI device100.

The memory230may include a model storage231. The model storage231may store a model (or an artificial neural network231a) which is learning or has learned via the learning processor240.

The learning processor240may cause the artificial neural network231ato learn learning data. A learning model may be used in the state of being mounted in the AI server200of the artificial neural network, or may be used in the state of being mounted in an external device such as the AI device100.

The learning model may be realized in hardware, software, or a combination of hardware and software. In the case in which a part or the entirety of the learning model is realized in software, one or more instructions constituting the learning model may be stored in the memory230.

The processor260may deduce a result value for newly input data using the learning model, and may generate a response or a control instruction based on the deduced result value.

FIG. 3illustrates an AI system according to an example embodiment.

Referring toFIG. 3, in the AI system1, at least one of the AI server200, a robot100a, an autonomous vehicle100b, an XR device100c, a smartphone100d, and a home appliance100eis connected to a cloud network10. Here, the robot100a, the autonomous vehicle100b, the XR device100c, the smartphone100d, and the home appliance100e, to which AI technologies are applied, may be referred to as AI devices100ato100e.

The cloud network10may constitute a part of a cloud computing infrastructure, or may refer to a network present in the cloud computing infrastructure. Here, the cloud network10may be configured using a 3G network, a 4G or long term evolution (LTE) network, or a 5G network, for example.

That is, respective devices100ato100eand200constituting the AI system1may be connected to each other via the cloud network10. In particular, respective devices100ato100eand200may communicate with each other via a base station, or may perform direct communication without the base station.

The AI server200may include a server which performs AI processing and a server which performs an operation with respect to big data.

The AI server200may be connected to at least one of the robot100a, the autonomous vehicle100b, the XR device100c, the smartphone100d, and the home appliance100e, which are AI devices constituting the AI system1, via cloud network10, and may assist at least a part of AI processing of connected the AI devices100ato100e.

At this time, instead of the AI devices100ato100e, the AI server200may cause an artificial neural network to learn according to a machine learning algorithm, and may directly store a learning model or may transmit the learning model to the AI devices100ato100e.

At this time, the AI server200may receive input data from the AI devices100ato100e, may deduce a result value for the received input data using the learning model, and may generate a response or a control instruction based on the deduced result value to transmit the response or the control instruction to the AI devices100ato100e.

Alternatively, the AI devices100ato100emay directly deduce a result value with respect to input data using the learning model, and may generate a response or a control instruction based on the deduced result value.

Hereinafter, various example embodiments of the AI devices100ato100e, to which the above-described technology is applied, will be described. Here, the AI devices100ato100eillustrated inFIG. 3may be specific example embodiments of the AI device100illustrated inFIG. 1.

The autonomous vehicle100bmay be realized into a mobile robot, a vehicle, or an unmanned air vehicle, for example, through the application of AI technologies.

The autonomous vehicle100bmay include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip realized in hardware. The autonomous driving control module may be a constituent element included in the autonomous vehicle1200b, but may be a separate hardware element outside the autonomous vehicle1200bso as to be connected thereto.

The autonomous vehicle100bmay acquire information on the state of the autonomous vehicle1200busing sensor information acquired from various types of sensors, may detect or recognize the surrounding environment and an object, may generate map data, may determine a movement route and a driving plan, or may determine an operation.

Here, the autonomous vehicle100bmay use sensor information acquired from at least one sensor among a lidar, a radar, and a camera in the same manner as the robot1200ain order to determine a movement route and a driving plan.

In particular, the autonomous vehicle100bmay recognize the environment or an object with respect to an area outside the field of vision or an area located at a predetermined distance or more by receiving sensor information from external devices, or may directly receive recognized information from external devices.

The autonomous vehicle100bmay perform the above-described operations using a learning model configured with at least one artificial neural network. For example, the autonomous vehicle100bmay recognize the surrounding environment and the object using the learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the autonomous vehicle100b, or may be learned in an external device such as the AI server200.

At this time, the autonomous vehicle100bmay generate a result using the learning model to perform an operation, but may transmit sensor information to an external device such as the AI server200and receive a result generated by the external device to perform an operation.

The autonomous vehicle100bmay determine a movement route and a driving plan using at least one of map data, object information detected from sensor information, and object information acquired from an external device, and a drive part may be controlled to drive the autonomous vehicle100baccording to the determined movement route and driving plan.

The map data may include object identification information for various objects arranged in a space (e.g., a road) along which the autonomous vehicle100bdrives. For example, the map data may include object identification information for stationary objects, such as streetlights, rocks, and buildings, and movable objects such as vehicles and pedestrians. Then, the object identification information may include names, types, distances, and locations, for example.

In addition, the autonomous vehicle100bmay perform an operation or may drive by controlling the drive part based on user control or interaction. At this time, the autonomous vehicle100bmay acquire interactional intention information depending on a user operation or voice expression, and may determine a response based on the acquired intention information to perform an operation.

In the present disclosure, a vehicle may include a sensor and an electronic apparatus. The electronic apparatus may recognize a face of a passenger in the vehicle based on sensing information acquired from the sensor, acquire information on a position of the passenger and information on a position of a terminal in the vehicle, and store, when a difference between the position of the passenger and the position of the vehicle is within a preset distance, information on the recognized face in association with the terminal based on the acquired information. The sensor may include at least one of a radar sensor, a lidar sensor, and an image sensor such as a camera.

The electronic apparatus may recognize a face of a passenger based on the sensing information acquired from the sensor.

The electronic apparatus may acquire information on a position of the passenger and information on a position of a terminal. When a difference between the position of the passenger and the position of the vehicle is within a preset distance, the electronic apparatus may store information on the recognized face in association with the terminal based on the acquired information.

The electronic apparatus may recognize a first terminal in the vehicle. The electronic apparatus may determine whether a first face associated with the first terminal is present among faces of one or more passengers in the vehicle, based on first face information stored in association with the first terminal. As a determination result, when the first face is absent, the electronic apparatus may store information on a second face among the faces of the one or more passengers in association with the first terminal.

The electronic apparatus may transmit the information on the recognized face to one or more terminals in the vehicle through a communication part. The electronic apparatus may store the information on the recognized face in association with a terminal among the one or more terminals, based on information input in the one or more terminals.

The electronic apparatus may acquire information on a voice of the passenger and store the information on the voice in association with the terminal.

The electronic apparatus may recognize a first face of a first passenger. The electronic apparatus may recognize a first terminal associated with the recognized first face based on terminal information and face information correspondingly stored in a database. The electronic apparatus may establish communication connection with the first terminal.

The electronic apparatus may recognize faces of a plurality of passengers. The electronic apparatus may recognize terminals respectively associated with the recognized faces based on terminal information and face information correspondingly stored in a database. The electronic apparatus may determine priorities for communication connection with the terminals. The electronic apparatus may establish communication connection with the terminals based on the determined priorities.

The electronic apparatus may determine priorities for communication connection with the terminals based on information on the recognized faces.

The electronic apparatus may establish communication connection with a first terminal of a first passenger. When a preset event occurs in a second terminal of a second passenger, the electronic apparatus may determine whether face information associated with the second terminal is preset passenger face information. As a determination result, when the face information associated with the second terminal is the preset passenger face information, the electronic apparatus may establish communication connection with the second terminal.

The electronic apparatus may establish communication connection with the first terminal. The electronic apparatus may allow only a passenger having a face associated with the first terminal to control the first terminal, based on face information stored in association with the first terminal.

FIG. 4illustrates an operation of an electronic apparatus according to an example embodiment.

An electronic apparatus400may be included in a vehicle. The vehicle may be, for example, an autonomous vehicle.

The electronic apparatus400may recognize a face412of a passenger410. When a terminal414is recognized as a terminal possessed by the passenger410, the electronic apparatus400may store information on the recognized face412in association with the terminal414. Specifically, the electronic apparatus400may store information on the face412of the passenger410and information on the terminal414in a database as mutually corresponding information.

In an example embodiment, the electronic apparatus400may recognize the face412of the passenger410through a sensor. The electronic apparatus400may recognize whether the terminal414is a terminal possessed by the passenger410. For example, when a distance between a position of the passenger410and a position of the terminal414is within a preset range, the electronic apparatus400may recognize the terminal414as the terminal possessed by the passenger410. When the terminal414is recognized as the terminal possessed by the passenger410, the electronic apparatus400may store the information on the face412of the passenger410in association with the terminal414.

In another example embodiment, the electronic apparatus400may recognize the face412of the passenger410and the terminal414belonging to the passenger410through a sensor. For example, the electronic apparatus400may use an image sensor of the vehicle to recognize the face412of the passenger410and the terminal414belonging to the passenger410. Also, the electronic apparatus400may store the information on the face412of the passenger410in association with the terminal414.

FIG. 5is a flowchart illustrating an example of an operation method of an electronic apparatus.

In operation S510, the electronic apparatus400may recognize a face of a passenger in a vehicle through a sensor. Specifically, an in-vehicle sensor may sense the passenger and the face of the passenger. The electronic apparatus400may recognize the face of the passenger by acquiring a sensing result from the in-vehicle sensor.

In operation S520, the electronic apparatus400may acquire information on a position of the passenger and information on a position of a terminal in the vehicle.

The electronic apparatus400may acquire the information on the position of the passenger through a sensor. Specifically, the electronic apparatus400may use an image sensor or a distance sensor to acquire the information on the position of the passenger in the vehicle. In one example, the electronic apparatus400may acquire information on coordinates of the position of the passenger in the vehicle. In another example, the electronic apparatus400may acquire information on a seat of the passenger in the vehicle.

In an example embodiment, the electronic apparatus400may acquire information on a position of a terminal through a sensor. Specifically, the electronic apparatus400may use an image sensor or a distance sensor to acquire the information on the terminal in the vehicle. In one example, the electronic apparatus400may acquire information on coordinates of the terminal in the vehicle. In another example, the electronic apparatus400may acquire information on a seat in which the terminal is located in the vehicle.

In another example embodiment, the electronic apparatus400may acquire information on a position of a terminal through triangulation. In one example, the electronic apparatus400may use Bluetooth communication to acquire the information on the coordinates of the position of the terminal in the vehicle through the triangulation for the terminal. In another example, the electronic apparatus400may use a distance sensor to acquire the information on the coordinates of the position of the terminal in the vehicle through the triangulation for the terminal.

In the present disclosure, the terminal may be implemented as a computer or a portable terminal capable of accessing a server or another terminal through a network. Here, the computer may include, for example, a laptop computer, a desktop computer, and a notebook equipped with a web browser. The portable terminal may be a wireless communication device ensuring a portability and a mobility, and include any type of handheld wireless communication device, for example, a tablet PC, a smartphone, a communication-based terminal such as international mobile telecommunication (IMT), code division multiple access (CDMA), W-code division multiple access (W-CDMA), and long term evolution (LTE).

In operation S530, when a difference between the position of the passenger and the position of the terminal is within a preset distance, the electronic apparatus400may store information on the recognized face in association with the terminal based on the information acquired in operation S520. Specifically, when the difference between the position of the passenger and the position of the terminal is within the preset distance, the electronic apparatus400may recognize the terminal as a terminal possessed by the passenger. Accordingly, the electronic device400may store the information on the face of the passenger to be associated with the terminal.

When the terminal is recognized as a terminal possessed by the passenger, the electronic apparatus400may store the information on the face of the passenger and the information on the terminal in a database as mutually corresponding information. For example, the electronic apparatus400may assign the same ID to the information on the face of the passenger and the information on the terminal and store the information in the database.

In another example embodiment, the electronic apparatus400may recognize a face of a passenger in a vehicle through a sensor and transmit information on the recognized face to one or more terminals in the vehicle. Thereafter, the electronic apparatus400may store information on the recognized face of the passenger in association with a terminal among the one or more terminals based on information input in the one or more terminals. A related example will be described with reference toFIG. 6.

The electronic apparatus400may acquire information on a voice of the passenger and store the information on the voice of the passenger in association with a terminal of the passenger. In one example, the passenger may input information on the voice of the passenger through the terminal of the passenger. In this example, the electronic apparatus400may acquire the information on the voice of the passenger through the terminal of the passenger and store the information on the voice of the passenger in association with the terminal of the passenger. In another example, the electronic apparatus400may acquire information on the voice of the passenger through an in-vehicle sensor. When the terminal is recognized as a terminal possessed by the passenger, the electronic apparatus400may store the information on the voice of the passenger in association with the terminal of the passenger. Also, the electronic apparatus400may store the information on the face of the passenger and the information on the voice of the passenger in association with the terminal of the passenger.

As such, the electronic apparatus400may store the information on the face of the passenger in association with the terminal of the passenger. Through this, the electronic apparatus400may improve passenger's convenience in a vehicular environment using face information and terminal information stored in association with each other. For example, the electronic apparatus400may recognize the terminal of the passenger by recognizing the face of the passenger and establish communication connection with the terminal of the passenger by recognizing the face of the passenger, which may lead to enhanced passenger's convenience in the vehicular environment.

FIG. 6illustrates an electronic apparatus storing information on a face of a passenger in association with a terminal according to an example embodiment.

An electronic apparatus600may recognize a face of a passenger in a vehicle and transmit information on the recognized face to one or more terminals610and620in the vehicle. For example, when a terminal to be associated with the recognized face of the passenger is not recognized, the electronic apparatus600may transmit the information on the recognized face to the one or more terminals610and620. Also, the electronic apparatus600may request the one or more terminals610and620to select the information on the recognized face.

One or more passengers may select face information provided to the one or more terminals610and620. Specifically, the one or more passengers may select face information determined as matching a face of the corresponding passenger from information of a plurality of faces recognized by the electronic apparatus600and input the selected face information through the one or more terminals610and620. For example, a first passenger may select first face information612as his or her face information from the information on the plurality of faces recognized by the electronic apparatus600and input the selected first face information612to the terminal610of the first passenger. Also, a second passenger may select second face information622as his or her face information from the information on the plurality of faces recognized by the electronic apparatus600and input the selected second face information622to the terminal620of the second passenger.

The electronic apparatus600may store the information on the recognized face in association with a terminal among one or more terminals based on the information input to the one or more terminals610and620. For example, the electronic apparatus600may store the first face information612in association with the terminal610of the first passenger, and store the second face information622in association with the terminal620of the second passenger.

FIG. 7is a flowchart illustrating another example of an operation method of an electronic apparatus.

In operation S710, the electronic apparatus400may recognize a first terminal in a vehicle. The electronic apparatus400may recognize the first terminal that has been communication-connected with the electronic apparatus400in the vehicle. For example, the electronic apparatus400may recognize the first terminal that has been paired with the electronic apparatus400through Bluetooth connection.

In operation S720, the electronic apparatus400may determine whether a first face associated with the first terminal is present among faces of one or more passengers in the vehicle based on first face information stored in association with the first terminal recognized in operation S710.

The electronic apparatus400may recognize the first face information stored in association with the first terminal recognized in operation S710. Specifically, since the first face information of the first passenger previously recognized by the electronic apparatus400is stored in association with the first terminal of the first passenger, the electronic apparatus400may recognize the first face information stored in association with the first terminal of the first passenger recognized in operation S710. Thereafter, the electronic apparatus400may determine whether the first face is present among the faces of the one or more passengers recognized in the vehicle. In other words, the electronic apparatus400may determine whether a face matching the first face is present among the faces of the one or more passengers.

In operation S730, as a determination result of operation S720, when the first face is absent, the electronic apparatus400may store information on a second face among the faces of the one or more passengers in association with the first terminal.

In an example embodiment, as a determination result of operation S720, when the first face is absent, the electronic apparatus400may determine whether the first face is present among the faces of the one or more passengers using a more precise facial recognition technique. For example, the first face of the first passenger may be partially deformed due to make-up or plastic surgery. In this example, the electronic apparatus400may recognize the second face into which the first face is partially deformed, from the faces of the one or more passengers using an anti-spoofing technique. Thus, the electronic apparatus400may store information on the second face in association with the first terminal. In other words, the electronic apparatus400may update existing information on the first face stored in association with the first terminal, with the information on the second face.

In another example embodiment, as a determination result of operation S720, when the first face is absent, the electronic apparatus400may identify the second passenger as an owner of the first terminal, and store information on the second face of the second passenger in association with the first terminal. For example, the owner of the first terminal may be changed from the first passenger to the second passenger. In this example, when a difference between a position of the first terminal and a position of the second passenger is within a preset distance, the electronic apparatus400may store information on the second face of the second passenger in association with the first terminal. In other words, the electronic apparatus400may update existing information on the first face stored in association with the first terminal, with the information on the second face.

As a determination result of operation S720, when the first face is present, the electronic apparatus400may maintain the information on the first face stored in association with the first terminal without updating.

FIG. 8illustrates an operation of an electronic apparatus recognizing a face of a passenger according to an example embodiment.

An electronic apparatus800may recognize a face of a passenger of a vehicle, recognize a terminal associated with the recognized face based on face information and terminal information correspondingly stored in a database, and establish wireless communication connection with the recognized terminal.

Referring toFIG. 8, the electronic apparatus800may recognize a face812of a first passenger located in a vehicle using a sensor. The electronic apparatus800may search a database850to recognize a terminal814corresponding to the recognized face812. Specifically, the database850may include face information of passengers and terminal information corresponding to the face information. For example, the database850may include information852on the face812of the first passenger and information854of the terminal814of a first passenger. Accordingly, the electronic apparatus800may recognize the terminal814associated with the face812of the first passenger based on information stored in the database850, and establish communication connection with the terminal814. For example, the electronic apparatus800may establish Bluetooth-pairing connection with the terminal814.

The electronic apparatus800may execute an application in a terminal in response to the communication connection with the terminal of the passenger being established. For example, the electronic apparatus800may execute a music application in the terminal to play music through a speaker in the vehicle. Also, the electronic apparatus800may recognize the face of the passenger and thus, execute an application in the terminal using a face recognition result. For example, the electronic apparatus800may recognize an emotion state of the passenger by recognizing the face of the passenger and play music appropriate for the recognized emotion state by executing a music application in the terminal.

The electronic apparatus800may recognize a voice of the passenger, recognize a terminal associated with the recognized voice based on voice information and terminal information correspondingly stored in a database, and establish communication connection with the recognized terminal. For example, in response to a speech “connect” of the first passenger being recognized, the electronic apparatus800may establish Bluetooth-pairing connection with the terminal of the first passenger.

The electronic apparatus800may recognize the face of the passenger and control at least one device in the vehicle based on a recognition result. For example, the electronic apparatus800may recognize an emotion state of the passenger based on a face recognition result of the passenger and adjust a color of a lighting device around the passenger based on the recognized emotion state.

The electronic apparatus800may set an authority to control the terminal based on face information associated with the terminal to which the communication connection is established. Specifically, the electronic apparatus800may recognize, as an owner of the terminal, a passenger having a face associated with the terminal to which the communication connection is established. Thus, the electronic apparatus800may allow only the passenger recognized as the owner to control the terminal. For example, the electronic apparatus800may allow only the passenger recognized as the owner of the terminal to which the communication connection is established, to input destination information to the terminal.

The electronic apparatus800may recognize schedule information of the passenger stored in each device in the vehicle, and input place information corresponding to a first schedule to a navigator as the destination information based on the recognized schedule information. In addition, the electronic apparatus800may recognize a face of a driver in the vehicle and input place information stored in a terminal associated with the recognized face, to the navigator as the destination information. Also, the electronic apparatus800may recognize the face of the passenger and recognize an emotion state of the passenger based on the recognized face. Thus, the electronic apparatus800may input a place suitable for the emotion state to the navigator as the destination information.

The electronic apparatus800may establish communication connection with the terminal of the passenger. When a call is received in the terminal, the electronic apparatus800may recognize a voice command of the passenger to provide a call function through a speaker of the terminal or provide the call function through a speaker in the vehicle.

The electronic apparatus800may recognize the voice of the passenger and establish communication connection with a terminal associated with the voice of the passenger. In addition, the electronic apparatus800may recognize a voice command of the passenger and execute an application of the communication-connected terminal. Also, the electronic apparatus800may recognize an emotion state of the passenger by recognizing the face of the passenger and execute an application of the terminal based on the recognized emotion state. For example, the electronic apparatus800may search for a music list suitable for the emotion state of the passenger and suggest the music list to the passenger.

The electronic apparatus800may control an in-vehicle device by recognizing the face of the passenger. In one example, the electronic apparatus800may recognize an age of the passenger by recognizing the face of the passenger. When the passenger is at least middle-aged, the electronic apparatus800may increase a font size of the navigator in the vehicle. In another example, the electronic apparatus800may recognize an age of the passenger by recognizing the face of the passenger and provide an image suitable for the age of the passenger through a display in front of the passenger.

FIG. 9is a flowchart illustrating another example of an operation method of an electronic apparatus.

In operation S910, the electronic apparatus400may recognize faces of a plurality of passengers located in a vehicle. Specifically, the electronic apparatus400may recognize the faces of the plurality of passengers using a sensor.

In operation S920, the electronic apparatus400may recognize terminals respectively associated with the recognized faces based on prestored face information and terminal information. Specifically, the database may include face information of the passengers and terminal information corresponding to the face information. Thus, the electronic apparatus400may recognize the terminals associated with the recognized faces based on the face information and terminal information stored in the database.

In operation S930, the electronic apparatus400may determine priorities for communication connection with the recognized terminals.

In an example embodiment, the electronic apparatus400may determine priorities for communication connection with the terminals corresponding to the recognized faces based on information on the recognized faces. In one example, the electronic apparatus400may determine communication connection with a terminal of a passenger having a designated face, to be a first priority based on the information on the recognized faces. In this example, the electronic apparatus400may recognize a face of a driver among the recognized faces based on the information on the recognized faces and determine communication connection with a terminal of the driver to be a first priority. In another example, the electronic apparatus400may determine priorities for communication connection with the terminals of the passengers based on ages of the passengers determined based on information on the recognized faces. In this example, an electronic apparatus900may determine priorities for communication connection by giving a higher priority to a terminal of an elderly passenger among the passengers. In another example, the electronic apparatus400may determine priorities for communication connection with the terminals of the passengers based on states of the passengers determined based on information on the recognized faces. In this example, the electronic apparatus400may recognize that the first passenger is in an emergency state based on the information on the recognized faces, and determine the priorities for communication connection by giving a higher priority to the terminal of the first passenger.

In another example embodiment, the electronic apparatus400may determine priorities for communication connection with terminals of passengers based on information stored in the terminals. In one example, the electronic apparatus400may determine priorities for communication connection with the terminals based on schedule information stored in the terminals. In another example, the electronic apparatus400may determine priorities for communication connection with the terminals based on destination information stored in the terminals. For example, when destinations of the passengers stored in the terminals are different in a shared-car environment, the electronic apparatus400may determine priorities for communication connection by giving a higher priority to a terminal of a passenger scheduled to get off first.

In operation S940, the electronic apparatus400may establish the communication connections with the terminals based on the determined priorities. The electronic apparatus400may sequentially establish the communication connections with the terminals based on the determined priorities, and establish communication connection with a highest priority for a terminal having the highest priority.

For example, the electronic apparatus400may determine ages of passengers located in a vehicle by recognizing faces of the passengers. When a vehicle accident occurs, the electronic apparatus400may sequentially establish communication connections with terminals of the passengers in an order of the ages and transmit an emergency message to phone numbers stored in the terminals of the passengers.

FIG. 10illustrates an electronic apparatus establishing communication connection by changing a terminal according to an example embodiment.

An electronic apparatus1000may recognize a face1012of a first passenger and recognize a terminal1014of the first passenger corresponding to the face1012based on prestored face information and terminal information. Also, the electronic apparatus1000may recognize a face1022of a second passenger and recognize a terminal1024of the second passenger corresponding to the face1022based on prestored face information and terminal information.

Referring to an upper portion ofFIG. 10, the electronic apparatus1000may preferentially establish communication connection with the terminal1014between the terminals1014and1024. For example, the electronic apparatus1000may establish Bluetooth communication connection with the terminal1014and play music through a speaker in the vehicle by executing a music application of the terminal1014.

Referring to the upper portion and a lower portion ofFIG. 10, the electronic apparatus1000may change a terminal corresponding to a target for communication connection from the terminal1014to the terminal1024. Specifically, when a preset event occurs in the terminal1024of the second passenger, the electronic apparatus1000may determine whether face information associated with the terminal1024is preset passenger face information. As a determination result, when the face information associated with the terminal1024is the preset passenger face information, the electronic apparatus1000may establish communication connection with the terminal1024following the terminal1014. For example, the electronic apparatus1000may recognize an event that the terminal1024receives a call. In this example, when the face information associated with the terminal1024is information on a face of a driver, the electronic apparatus1000may change a terminal corresponding to a target for communication connection from the terminal1014to the terminal1024. For example, the electronic apparatus1000may suspend executing the music application of the terminal1014and establish the Bluetooth communication connection with the terminal1024, thereby implementing a voice call of the second passenger corresponding to the driver through the speaker in the vehicle.

FIG. 11is a block diagram illustrating an electronic apparatus.

According to an example embodiment, an electronic apparatus1100may include an interface1110and a processor1120.FIG. 11illustrates only components of the electronic apparatus1100related to the present embodiment. However, it will be understood by those skilled in the art that other general-purpose components may be further included in addition to the components illustrated inFIG. 11. Since the electronic apparatus1100corresponds to the electronic apparatuses400,600,800, and1000, repeated description will be omitted.

The interface1110may acquire sensing information of a face of a passenger in a vehicle from a sensor. The interface1110may acquire information on a position of the passenger and information on a position of a terminal in the vehicle.

The processor1120may control an overall operation of the electronic apparatus1100and process data and a signal. The processor11201320may include at least one hardware unit. In addition, the processor1120may operate through at least one software module generated by executing program codes stored in a memory.

The processor1120may recognize the face of the passenger based on the sensing information acquired by the interface1110.

When a difference between the position of the passenger and the position of the vehicle is within a preset distance, the processor1120may store information on the recognized face in association with the terminal based on the information on the position of the passenger and the information on the position of the terminal acquired by the interface1110.

The processor1120may recognize a first terminal in the vehicle. The processor1120may determine whether a first face associated with the first terminal is present among faces of one or more passengers in the vehicle, based on first face information stored in association with the first terminal. As a determination result, when the first face is absent, the processor1120may store information on a second face among the faces of one or more passengers in association with the first terminal.

The processor1120may transmit the information on the recognized face to one or more terminals in the vehicle through a communication part. The processor1120may store the information on the recognized face in association with a terminal among the one or more terminals, based on information input in the one or more terminals.

The interface1110may acquire information on a voice of the passenger. The processor1120may store the information on the voice in association with the terminal.

The processor1120may recognize a first face of a first passenger. The processor1120may recognize a first terminal associated with the recognized first face based on terminal information and face information correspondingly stored in a database. The processor1120may establish communication connection with the first terminal.

The processor1120may recognize faces of a plurality of passengers. The processor1120may recognize terminals respectively associated with the recognized faces based on terminal information and face information correspondingly stored in a database. The processor1120may determine priorities for communication connection with the terminals. The processor1120may establish communication connection with the terminals based on the determined priorities.

The processor1120may determine priorities for communication connection with the terminals based on information on the recognized faces.

The processor1120may establish communication connection with the first terminal of the first passenger. When a preset event occurs in a second terminal of a second passenger, the processor1120may determine whether face information associated with the second terminal is preset passenger face information. As a determination result, when the face information associated with the second terminal is the preset passenger face information, the processor1120may establish communication connection with the second terminal.

The processor1120may establish communication connection with the first terminal. The processor1120may allow only a passenger having a face associated with the first terminal to control the first terminal, based on face information stored in association with the first terminal.

According to example embodiments, since an electronic apparatus stores information on a face of a passenger in association with a terminal of the passenger, it is possible to improve passenger's convenience in a vehicular environment using face information and terminal information stored in association with each other. For example, the electronic apparatus may recognize a terminal of a passenger by recognizing a face of the passenger and establish communication connection with a terminal of the passenger by recognizing the face of the passenger, which may lead to enhanced passenger's convenience in the vehicular environment.

Effects are not limited to the aforementioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

The devices in accordance with the above-described embodiments may include a processor, a memory which stores and executes program data, a permanent storage such as a disk drive, a communication port for communication with an external device, and a user interface device such as a touch panel, a key, and a button. Methods realized by software modules or algorithms may be stored in a computer-readable recording medium as computer-readable codes or program commands which may be executed by the processor. Here, the computer-readable recording medium may be a magnetic storage medium (for example, a read-only memory (ROM), a random-access memory (RAM), a floppy disk, or a hard disk) or an optical reading medium (for example, a CD-ROM or a digital versatile disc (DVD)). The computer-readable recording medium may be dispersed to computer systems connected by a network so that computer-readable codes may be stored and executed in a dispersion manner. The medium may be read by a computer, may be stored in a memory, and may be executed by the processor.

The present embodiments may be represented by functional blocks and various processing steps. These functional blocks may be implemented by various numbers of hardware and/or software configurations that execute specific functions. For example, the present embodiments may adopt direct circuit configurations such as a memory, a processor, a logic circuit, and a look-up table that may execute various functions by control of one or more microprocessors or other control devices. Similarly to that elements may be executed by software programming or software elements, the present embodiments may be implemented by programming or scripting languages such as C, C++, Java, and assembler including various algorithms implemented by combinations of data structures, processes, routines, or of other programming configurations. Functional aspects may be implemented by algorithms executed by one or more processors. In addition, the present embodiments may adopt the related art for electronic environment setting, signal processing, and/or data processing, for example. The terms “mechanism”, “element”, “means”, and “configuration” may be widely used and are not limited to mechanical and physical components. These terms may include meaning of a series of routines of software in association with a processor, for example.

The above-described embodiments are merely examples and other embodiments may be implemented within the scope of the following claims.