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Documentation Index

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The FaceNet Android app uses TensorFlow Lite models sourced from the deepface library to generate face embeddings. Two variants are available with different embedding dimensions.

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Model variants

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facenet.tflite

The standard FaceNet model that outputs 128-dimensional embeddings. Input shape: [1, 160, 160, 3] - 160x160 RGB face image Output shape: [1, 128] - 128-dimensional embedding vector Use case: Suitable for most face recognition tasks with lower memory footprint and faster inference.

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facenet_512.tflite

The extended FaceNet model that outputs 512-dimensional embeddings. Input shape: [1, 160, 160, 3] - 160x160 RGB face image Output shape: [1, 512] - 512-dimensional embedding vector Use case: Provides higher accuracy for face recognition at the cost of increased memory usage and slightly slower inference.
Both models require cropped face images of size 160x160 pixels as input. Face detection is performed separately using either Mediapipe or MLKit before feeding images to FaceNet.

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Model source

Both FaceNet models are sourced from the deepface library, a popular Python library for face recognition and facial attribute analysis.

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Conversion process

The models were converted from Keras format to TensorFlow Lite with FP16 optimization using the following Python scripts:

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Converting facenet.tflite

from deepface import DeepFace
from deepface.models.facial_recognition.Facenet import scaling
import tensorflow as tf

model = DeepFace.build_model("Facenet")
model.model.save("facenet.keras")

model = tf.keras.models.load_model("facenet.keras", custom_objects={
    "scaling": scaling
})
converter_fp16 = tf.lite.TFLiteConverter.from_keras_model(model)
converter_fp16.optimizations = [tf.lite.Optimize.DEFAULT]
converter_fp16.target_spec.supported_types = [tf.float16]
tflite_model_fp16 = converter_fp16.convert()

with open("facenet.tflite", "wb") as file:
    file.write(tflite_model_fp16)

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Converting facenet_512.tflite

from deepface import DeepFace
from deepface.models.facial_recognition.Facenet import scaling
import tensorflow as tf

model = DeepFace.build_model("Facenet512")
model.model.save("facenet512.keras")

model = tf.keras.models.load_model("facenet512.keras", custom_objects={
    "scaling": scaling
})
converter_fp16 = tf.lite.TFLiteConverter.from_keras_model(model)
converter_fp16.optimizations = [tf.lite.Optimize.DEFAULT]
converter_fp16.target_spec.supported_types = [tf.float16]
tflite_model_fp16 = converter_fp16.convert()

with open("facenet_512.tflite", "wb") as file:
    file.write(tflite_model_fp16)
The conversion process applies FP16 (half-precision floating point) optimization to reduce model size while maintaining accuracy.

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Choosing between models

To switch between the two FaceNet models in your app:
  1. Update the model path in FaceNet.kt:
// For facenet.tflite (128-dimensional)
interpreter =
    Interpreter(FileUtil.loadMappedFile(context, "facenet.tflite"), interpreterOptions)

// For facenet_512.tflite (512-dimensional)
interpreter =
    Interpreter(FileUtil.loadMappedFile(context, "facenet_512.tflite"), interpreterOptions)
  1. Update the embedding dimension in FaceNet.kt:
// For facenet.tflite
private val embeddingDim = 128

// For facenet_512.tflite
private val embeddingDim = 512
  1. Update the database schema in DataModels.kt:
@Entity
data class FaceImageRecord(
    @Id var recordID: Long = 0,
    @Index var personID: Long = 0,
    var personName: String = "",
    
    // Update dimensions to match your chosen model
    @HnswIndex(dimensions = 512)  // or 128 for facenet.tflite
    var faceEmbedding: FloatArray = floatArrayOf()
)

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Model architecture

FaceNet uses a deep convolutional neural network architecture based on the FaceNet paper. The model is trained using triplet loss to learn embeddings where:
  • Embeddings of the same person are close together in the embedding space
  • Embeddings of different people are far apart
  • Cosine similarity is used to measure distance between embeddings

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External resources

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