General

Before you can call the SDK functions, you must first initialize the SDK with your desired configuration options. These configuration options will ultimately impact the behaviour of the SDK, so be sure to read through what each one does carefully.

Once you have initialized the SDK, then proceed to the the License section.

Trueface::SDK::SDK()

Initialize the SDK using default ConfigurationOptions.

See

ConfigurationOptions

Trueface::SDK::SDK(const ConfigurationOptions &options)

Initialize the SDK using custom ConfigurationOptions.

Parameters

options – custom configuration options.

static std::string Trueface::SDK::getVersion()

Gets the version-build number of the SDK.

Returns

Version Number as a string.

enum Trueface::FacialRecognitionModel

Facial recognition models. To compare model performances, refer to our ROC curves. You can also find more information on our FAQ page. The current most accurate model is TFV5.

Values:

enumerator LITE

Our most lightweight model with fastest inference time but lowest accuracy, ideal for embedded systems or lightweight CPU only deployments, prototyping, and some 1 to 1 matching use cases.

enumerator LITE_V2

Lightweight model which has improved accuracy over the previous LITE model, though does have slightly greater inference time. Ideal for embedded systems or lightweight CPU only deployments, prototyping, and some 1 to 1 matching use cases.

enumerator FULL

Full TFV4 model which has better accuracy than the LITE model, but also has greater inference time. Ideal for GPU deployments and for 1 to N use cases. Note, TFV4 has now been deprecated and replaced by TFV5 which has better performance. Despite this, we will continue providing support for TFV4 for clients with existing collections.

enumerator TFV5

TFV5 is currently the highest accuracy model. Ideal for GPU deployments and for 1 to N use cases.

enum Trueface::ObjectDetectionModel

Object detection models

Values:

enumerator ACCURATE

enumerator FAST

enum Trueface::FaceDetectionFilter

Filters the detected faces based on score thresholds obtained from ROC curve.

Values:

enumerator HIGH_RECALL

Filter the detected faces based on a low score threshold. Limits false negatives (does not detect a face), but may have more false positives (classifies a non-face as a face).

enumerator HIGH_PRECISION

Filter the detected faces based on a high score threshold. Limits false positives (classifies a non-face as a face), but may have more false negatives (does not detect a face).

enumerator BALANCED

Filter the detected faces based on a medium score threshold to balance false positives and false negatives. We advise using this option most of the time.

enumerator UNFILTERED

Do not filter the detected faces by score. Will have a large number of false positives (classifies a non-face as a face).

enum Trueface::DatabaseManagementSystem

Database Management System for storing Faceprints

Values:

enumerator SQLITE

Use sqlite backend. Write Faceprints to local disk. Ideal for embedded systems or use cases where only one process connects to the database.

enumerator POSTGRESQL

Use a PostgreSQL backend. Ideal for distributed systems requiring synchronization.

enumerator NONE

Do not write Faceprints to disk, only store in ram. Warning, enrolled Faceprints will not be saved after the program terminates. Switching to a new collections will also delete all enrolled templates.

struct Trueface::EnableGPU

Enable GPU support (default is false). Note, GPU support requires a different version of the SDK. You are able to enable GPU for all modules or only specific modules.

Public Functions

inline EnableGPU(bool val)

Enable or disable GPU inference for all modules

Public Members

bool faceDetector = false

Enable GPU inference for face detection

bool faceRecognizer = false

Enable GPU inference for face recognition template generation

Trueface::EnableGPU::EnableGPU() = default

inline Trueface::EnableGPU::EnableGPU(bool val)

Enable or disable GPU inference for all modules

struct Trueface::InitializeModule

Initialize module in SDK constructor. By default, the SDK uses lazy initialization, meaning modules are only initialized when they are first used (on first inference). This is done so that modules which are not used do not load their models into memory, and hence do not utilize memory. The downside to this is that the first inference will be much slower as the model file is being decrypted and loaded into memory. Therefore, if you know you will use a module, choose to pre-initialize the module, which reads the model file into memory in the SDK constructor.

Public Members

bool faceDetector = false

Face detector

bool faceRecognizer = false

Face recognizer

bool objectDetector = false

Object detector

bool bodyposeEstimator = false

Bodypose estimator

bool liveness = false

Liveness

bool activeSpoof = false

Active spoof

bool landmarkDetector = false

106 face point landmark detector

struct Trueface::EncryptDatabase

Encrypt the biometric templates and identity strings when storing in the database using AES encryption. Note, enabling this option does add overhead to Faceprint enrollment as well as loading a collection from a database into memory. Enabling encryption does not increase the 1 to N identification time.

Public Members

bool enableEncryption = false

Enable database encryption. Must provide encryption key if encryption is enabled. If enabling encryption with PostgreSQL backend, it is strongly advised to require SSL for PostgreSQL connection.

std::string key

Encryption key. The key is hashed to a fixed length before being used for encryption.

struct Trueface::ConfigurationOptions

SDK configuration options

Public Members

int mobilePowerSave = 0

Android Only Power saving mode. 0 = all cores enabled (default). 1 = only little clusters enabled. 2 = only big clusters enabled.

int mobileThreads = 4

Mobile Only Set the number of threads used for inference. For non-mobile platforms, should use the OMP_NUM_THREADS environment variable.

size_t mobileAvailableMemory = 0

iOS Only SDK user must define how much memory is available for iOS application. Should be set using the following code: #include <os/proc.h> options.mobileAvailableMemory = os_proc_available_memory();

FacialRecognitionModel frModel = FacialRecognitionModel::TFV5

The model to be used for facial recognition (default is TFV5)

ObjectDetectionModel objModel = ObjectDetectionModel::ACCURATE

The model to be used for object detection (default is ACCURATE model)

int smallestFaceHeight = 40

The smallest face height that the face detector can detect. (default is 40 pixels, min value is 16 pixels). The face detector has a detection scale range of about 5 octaves. Ex. 40 pixels yields the detection scale range of ~40 pixels to 1280 (=40x2^5) pixels. If set to -1, will dynamically adjusts the face detection scale range from image-height/32 to image-height to ensure that large faces are detected in high resolution images. Increasing the Trueface::ConfigurationOptions.smallestFaceHeight will result in faster face detection.

FaceDetectionFilter fdFilter = FaceDetectionFilter::BALANCED

The face detection mode (default is VERSATILE). This configuration option is now deprecated and may be removed in future releases.

DatabaseManagementSystem dbms = DatabaseManagementSystem::SQLITE

Database management system for storing Faceprints (default is SQLITE)

std::string modelsPath = "./"

The directory path containing the model files

bool frVectorCompression = false

Improves the computation speed for 1 to 1 comparisons and 1 to N searches by compressing the feature vector and enabling additional optimizations.

EnableGPU enableGPU = {}

Enable GPU support (default is false). Note, GPU support requires a different version of the SDK. You are able to enable GPU for all modules or only specific modules.

unsigned int deviceIndex = 0

GPU device index

std::vector<unsigned int> batchSizes = {}

Specify the batch sizes which will be used, resulting in improved performance when switching between batch sizes. If the batch sizes are specified, then using a non-specified batch size will result in an exception being thrown. Note, GPU memory will be allocated for each of the specified batch sizes, so specifying too many batch sizes may result in an out-of-memory crash. Leave vector empty to support dynamic batch sizes. Dynamic batch sizes will result in a slowdown when switching between batch sizes. Ex. If processing 36 images in the following batches: 10, 10, 10, 6 there will be a slight slowdown when switching from the batch size of 10 to 6.

InitializeModule initializeModule = {}

Initialize specified modules in the SDK constructor (default uses lazy initialization).

EncryptDatabase encryptDatabase = {}

Encrypt the biometric templates and identity strings when storing in the database using AES encryption (default is disabled).

enum Trueface::ErrorCode

Error codes returned by methods

Values:

enumerator NO_ERROR

enumerator INVALID_LICENSE

enumerator FILE_READ_FAIL

enumerator UNSUPPORTED_IMAGE_FORMAT

enumerator UNSUPPORTED_MODEL

enumerator NO_FACE_IN_FRAME

enumerator FAILED

enumerator COLLECTION_CREATION_ERROR

enumerator DATABASE_CONNECTION_ERROR

enumerator ENROLLMENT_ERROR

enumerator MAX_COLLECTION_SIZE_EXCEEDED

enumerator NO_RECORD_FOUND

enumerator NO_COLLECTION_FOUND

enumerator COLLECTION_DELETION_ERROR

enumerator EXTREME_FACE_ANGLE

enumerator FACE_TOO_CLOSE

enumerator FACE_TOO_FAR

Deprecated

The following have been deprecated and may be removed in future releases of the SDK.